Micrornas expression signature for determination of tumors origin

ABSTRACT

The present invention provides a process for classification of specific cancers and tumors origin through the analysis of the expression patterns of specific microRNAs and nucleic acid molecules relating thereto. Classification according to a microRNA expression framework allows optimization of treatment, and determination of specific therapy.

CROSS REFERENCE TO RELATED APPLICATIONS

The present application is a Continuation-In-Part of Ser. No.12/532,941, filed Sep. 24, 2009, which is the U.S. National Stage ofPCT/IL2008/00396, filed Mar. 20, 2008, which claims priority to U.S.Provisional applications 60/907,266, filed Mar. 27, 2007, 60/929,244,filed Jun. 19, 2007, and 61/024,565, filed Jan. 30, 2008. The presentapplication is also a Continuation-In Part of PCT/IL2008/001525, filedNov. 20, 2008, which claims priority to U.S. Provisional ApplicationNos. 60/989,458, filed Nov. 21, 2008, 61/043,407, filed Apr. 9, 2008,and 61/073,774, filed Jun. 19, 2008. All of the priority applicationsare herein incorporated by reference in their entirety.

FIELD OF THE INVENTION

The present invention relates to methods for classification of cancersand tumors origin. Specifically the invention relates to microRNAmolecules associated with specific cancer, as well as various nucleicacid molecules relating thereto or derived therefrom.

BACKGROUND OF THE INVENTION

microRNAs (miRs, miRNAs) are a family of 18-24 nucleotide longnon-coding small RNAs, that suppress translation of target genes bybinding to their mRNA, thereby regulating the expression of at least 30%of all human genes. There are currently about 850 known human microRNAs.Though highly conserved throughout evolution, a significant proportionof them is primate specific. microRNAs association with cancer has beendemonstrated and several microRNAs have already been identified asoncogenes and tumor suppressors (He, H., et al., Proc Natl Acad Sci USA,2005. 102(52): p. 19075-80).

One of the major characteristics of microRNAs is their marked tissuespecificity. Many of them also exhibit temporal patterns of expression,suggesting that they play a critical role in specific tissues and inorgan development, function and maintenance.

The differential diagnosis of hepatic lesions that include primary livertumor and metastatic tumors is a frequent challenge in modern surgicalpathology. Hepatic malignancies are often found in patients withadvanced metastatic cancer. Identifying the origin of these tumors aswell as differentiating liver metastases from primary hepatocellularcarcinoma (HCC) is frequently required and poses a significant challengethat requires clinical-radiological correlation on top of carefulpathological evaluation. Thorough examination assisted by a panel ofimmunostains is required for the identification of the origin ofmetastases. Extensive work-up using modern pathological tools(immunohistochemistry, electron microscopy and molecular diagnosis) andadvanced imaging technology (computed tomography (CT), mammography andpositron emission tomography (PET)) have resulted in some improvementsin diagnosis. However, the primary site remains unknown in manypatients, even on autopsy. The appropriate management of such patientsis unclear and there is a high variability in clinical approaches,accompanied by poor prognosis in most cases.

The pathological characterization of brain malignancies remains adiagnostic challenge. Despite the advent of various high throughputgenomic level technologies, which allow multiple DNA sequences, mRNAs orproteins to be evaluated simultaneously and systematically, these havehad little impact on clinical procedures.

Differentiation between primary and metastatic tumors in the brain isoften encountered in pathological practice, since metastatic tumors tothe brain are quite frequent. The most common tumors to metastasize tothe brain originate in the lung, breast and skin (melanomas); theirrespective contributions to all central nervous system (CNS) metastasesare 30%, 20% and 10%. Although rare, choriocarcinoma disseminates to thebrain with a particularly high frequency. In autopsy studies, 24% ofcancer patients exhibited metastatic tumors in the CNS. Indeed, surgicalpathologists are regularly presented with specimens from patients with ahistory of systemic neoplasia but with findings that suggest a primaryintracranial tumor.

Therefore, there is a need for efficient and effective methods for thedifferentiation between primary and metastatic tumors.

SUMMARY OF THE INVENTION

The present invention provides specific nucleic acid sequences that areused for the identification, classification and diagnosis of cancers andtumor origin. The nucleic acid sequences can also be used for thedifferentiation between primary and metastatic tumors based on theexpression pattern of a biological sample.

According to one aspect, the present invention provides a method ofclassifying a specific cancer, the method comprising: obtaining abiological sample from a subject; determining an expression profile of anucleic acid sequence selected from the group consisting of SEQ ID NOS:1-33, a fragment thereof, or a sequence having at least about 80%identity thereto from said sample; and comparing said expression profileto a reference expression profile, wherein the results of saidcomparison allows for classification of said specific cancer.

According to certain embodiments, said cancer is selected from the groupconsisting of liver cancer, brain cancer and gastrointestinal (GI)cancer.

According to one embodiment, said liver cancer is hepatocellularcarcimoma (HCC).

According to certain embodiments, said GI cancer is selected from thegroup consisting of colon, pancreas and stomach cancer.

According to certain embodiments, said brain cancer is selected from thegroup consisting of glioblastoma, astrocytoma and oligodendroglioma.

The invention further provides a method for identifying liver cancer,the method comprising: obtaining a biological sample from a subject;determining an expression profile of a nucleic acid sequence selectedfrom the group consisting of SEQ ID NOS: 13-6, 32-33, a fragment thereofand a sequence having at least about 80% identity thereto from saidsample; and comparing said expression profile to a reference expressionprofile, wherein the comparison of said expression profile to saidreference expression profile allows for the identification of said livercancer.

The invention further provides a method to distinguish betweenhepatocellular carcimoma (HCC) and metastasis to the liver, the methodcomprising: obtaining a biological sample from a subject; determining anexpression profile of a nucleic acid sequence selected from the groupconsisting of SEQ ID NOS: 6-13, 32-33, a fragment thereof and a sequencehaving at least about 80% identity thereto from said sample; andcomparing said expression profile to a reference expression profile,wherein the comparison of said expression profile to said referenceexpression profile is indicative of hepatocellular carcimoma (HCC) ormetastasis to the liver.

According to some embodiments the nucleic acid sequence is selected fromthe group consisting of SEQ ID NOS: 6, 8, 10, 12, a fragment thereof anda sequence having at least about 80% identity thereto, whereinrelatively high expression levels of any of said nucleic acid sequence,as compared to said reference expression profile, is indicative ofmetastasis to the liver.

According to one embodiment, the liver metastasis is adenocarcinoma.

The invention further provides a method for identifying brain cancer,the method comprising: obtaining a biological sample from a subject;determining an expression profile of a nucleic acid sequence selectedfrom the group consisting of SEQ ID NOS: 14-27, a fragment thereof and asequence having at least about 80% identity thereto from said sample;and comparing said expression profile to a reference expression profile,wherein the comparison of said expression profile to said referenceexpression profile allows for the identification of said brain cancer.

The invention further provides a method to distinguish between primarybrain tumor and metastasis to the brain, the method comprising:obtaining a biological sample from a subject; determining an expressionprofile of a nucleic acid sequence selected from the group consisting ofSEQ ID NOS: 14-27, a fragment thereof and a sequence having at leastabout 80% identity thereto from said sample; and comparing saidexpression profile to a reference expression profile, wherein thecomparison of said expression profile to said reference expressionprofile is indicative of primary brain tumor or metastasis to the brain.

According to some embodiments the nucleic acid sequence is selected fromthe group consisting of SEQ ID NOS: 14, 20, a fragment thereof and asequence having at least about 80% identity thereto, wherein relativelyhigh expression levels of any of said nucleic acid sequence, as comparedto said reference expression profile, is indicative of primary braintumor.

The invention further provides a method to distinguish between primarybrain tumor and other primary cancers, the method comprising: obtaininga biological sample from a subject; determining an expression profile ofa nucleic acid sequence selected from the group consisting of SEQ IDNOS: 14-27, a fragment thereof and a sequence having at least about 80%identity thereto from said sample; and comparing said expression profileto a reference expression profile, wherein the comparison of saidexpression profile to said reference expression profile is indicative ofprimary brain tumor or other primary cancers.

The invention further provides a method for identifying agastrointestinal (GI) cancer, the method comprising: obtaining abiological sample from a subject; determining an expression profile of anucleic acid sequence selected from the group consisting of SEQ ID NOS:1-5, a fragment thereof and a sequence having at least about 80%identity thereto from said sample; and comparing said expression profileto a reference expression profile, wherein the comparison of saidexpression profile to said reference expression profile allows for theidentification of said GI cancer.

The invention further provides a method to distinguish between primaryGI and non-GI tumor, the method comprising: obtaining a biologicalsample from a subject; determining an expression profile of a nucleicacid sequence selected from the group consisting of SEQ ID NOS: 1-5, afragment thereof and a sequence having at least about 80% identitythereto from said sample; and comparing said expression profile to areference expression profile, wherein the comparison of said expressionprofile to said reference expression profile is indicative of primary GIor non-GI tumor.

According to some embodiments, said primary GI tumor is selected fromthe group consisting of colon, pancreas and stomach tumor.

According to other embodiments, said non-GI tumor is selected from thegroup consisting of lung and breast tumor.

According to some embodiments, said sample is selected from the groupconsisting of bodily fluid, a cell line and a tissue sample. Accordingto other embodiments, said tissue is a fresh, frozen, fixed,wax-embedded or formalin fixed paraffin-embedded (FFPE) tissue.

The classification method of the present invention further comprises aclassifier algorithm, said classifier algorithm is selected from thegroup consisting of logistic regression classifier, linear regressionclassifier, nearest neighbor classifier (including K nearest neighbors),neural network classifier, Gaussian mixture model (GMM) classifier andSupport Vector Machine (SVM) classifier. The classifier may use adecision tree structure (including binary tree) or a voting (includingweighted voting) scheme to compare one or more models which compare oneor more classes to other classes.

According to some embodiments the nucleic acid sequence expressionprofile is determined by a method selected from the group consisting ofnucleic acid hybridization and nucleic acid amplification. According tosome embodiments the nucleic acid hybridization is performed using asolid-phase nucleic acid biochip array or in situ hybridization.

According to some embodiments the nucleic acid amplification method isreal-time PCR. The real-time PCR method may comprise forward and reverseprimers. According to some embodiments the forward primer comprises asequence selected from the group consisting of SEQ ID NOS: 34-38.According to some embodiments the reverse primer comprises SEQ ID NO:44.

According to additional embodiments the real-time PCR method furthercomprises a probe. According to some embodiments the probe comprises asequence selected from the group consisting of a sequence that iscomplementary to a sequence selected from SEQ ID NOS: 1-33; a fragmentthereof and a sequence having at least about 80% identity thereto.According to additional embodiments the probe comprises a sequenceselected from the group consisting of SEQ ID NOS: 39-43.

According to another aspect, the present invention provides a kit forcancer classification, said kit comprising a probe comprising a sequenceselected from the group consisting of a sequence that is complementaryto a sequence selected from SEQ ID NOS: SEQ ID NOS: 1-33; a fragmentthereof and a sequence having at least about 80% identity thereto.

According to certain embodiments, said cancer is selected from the groupconsisting of liver cancer, brain cancer and gastrointestinal (GI)cancer.

According to some embodiments, said cancer is brain cancer. According tocertain embodiments, said probe comprises a sequence selected from thegroup consisting of SEQ ID NOS: 39-43. According to other embodiments,said the kit further comprises a forward primer selected from the groupconsisting of SEQ ID NOS: 34-38. According to other embodiments, saidthe kit further comprises a reverse primer comprising SEQ ID NO: 44.

These and other embodiments of the present invention will becomeapparent in conjunction with the figures, description and claims thatfollow.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 demonstrates the identification of non-HCC epithelial tumorsamples and metastases using microRNA biomarkers. A) Expression levels(microarray data) of hsa-miR-122a (SEQ ID NO: 32) and hsa-miR-200b (SEQID NO: 12) in 30 HCC samples (squares), 63 non-HCC primary tumors ofepithelial origin (diamonds), 46 samples of adenocarcinoma metastases tothe liver of known origin (circles) and 5 samples of adenocarcinomametastases to the liver of unknown origin (stars). Expression level ofhsa-miR-122a is high in all samples taken from liver, including primaryand metastases. Expression level of hsa-miR-200b is high in samples ofnon-liver origin, both primary and metastases. B) Expression levels(microarray data) of hsa-miR-141 (SEQ ID NO: 6) and hsa-miR-200c (SEQ IDNO: 8) in the same samples. The solid line marks the line where C′₁≡[log2(hsa-miR-141)+log 2(hsa-miR-200c)]=18, the dashed lines mark C₁=20(upper line) and C₁=16 (lower line). All samples with C₁<16 are HCC, andall samples with C₁>20 are non-HCC. The sharp threshold at C₁=18 isaccurate in >97% of 144 samples. C) Expression levels (qRT-PCR data, 50minus normalized C) of hsa-miR-122a and hsa-miR-200b in 5 HCC samples(squares), 19 non-HCC primary tumors of epithelial origin (diamonds),and 7 samples of adenocarcinoma metastases to the liver of known origin(circles). Expression level of hsa-miR-122a is high in all samples takenfrom liver, including primary and metastases. D) Expression levels(qRT-PCR) of hsa-miR-141 and hsa-miR-200c in the same samples. The solidline marks the line where C_(RT)≡[(hsa-miR-141)+(hsa-miR-200c)]=34. Withthe exception of one renal cell carcinoma metastasis to the liver, allsamples with C_(RT)<34 are HCC, and all samples with C_(RT)>34 arenon-HCC.

FIG. 2 demonstrates the identification of gastro-intestinal tumors andmetastases using a combination of two microRNA biomarkers. A) Expressionlevels of hsa-miR-194 (SEQ ID NO: 1) and hsa-miR-205 (SEQ ID NO: 4) in24 primary tumor samples of gastrointestinal (GI) origin (squares) and39 primary tumors from breast or lung (diamonds). The dashed gray linemarks the line where (hsa-miR-205)=(hsa-miR-194)/2 (see methods). B)Expression levels of hsa-miR-194 and hsa-miR-205 in 42 samples of livermetastases from GI origin (circles) and 4 metastases to the liver ofnon-GI origin (stars).

FIG. 3 demonstrates the identification of non-HCC epithelial tumorsamples and metastases using a combination of two microRNA biomarkers.A) Expression levels of hsa-miR-200a (SEQ ID NO: 10) and hsa-miR-200b(SEQ ID NO: 12) in 30 HCC samples (squares) and 63 non-HCC primarytumors of epithelial origin (diamonds). The solid gray line marks theline where Cab≡[log 2(hsa-miR-200a)+log 2(hsa-miR-200b)]=17.7. Only fourHCC samples have Cab>17.7, and only three non-HCC lung tumor sampleshave Cab<17.7. B) Expression levels of hsa-miR-200a and hsa-miR-200b in46 samples of adenocarcinoma metastases to the liver of known origin(circles) and 5 samples of adenocarcinoma metastases to the liver ofunknown origin (stars). The gray line marks Cab=17.7. Only threeadenocarcinoma metastases samples (one from breast cancer origin and twofrom unknown origin) have Cab<17.7.

FIG. 4 demonstrates the identification of metastatic brain tumors usingmicroRNA microarray data. A) Expression levels of hsa-miR-124a (SEQ IDNO: 16) and hsa-miR-219-5p (SEQ ID NO: 24) in 15 brain primary tumorsincluding GBM (squares), astrocytoma (triangles), oligodendroglioma(upside down triangles); 187 primary tumors from other tissues(diamonds), 50 brain metastases originating from various tissues(circles) and 2 normal brain samples (stars). Expression levels ofhsa-miR-124 and hsa-miR-219-5p are higher in brain primary tumorscompared to primary tumors from other tissues. The solid line marks theline where C₀[log 2(hsa-miR-124)+log 2(hsa-miR-219-5p)]=16.8, andprovides perfect separation between brain primary and other primarytumors. The expression levels of hsa-miR-124 and hsa-miR-219-5p inmetastatic samples span a wide range on both sides of the separatingline. B) Expression levels of hsa-miR-9* (SEQ ID NO: 20) and hsa-miR-92b(SEQ ID NO: 14) in the same samples. Expression levels of thesemicroRNAs are high in brain primary tumors but are low in all othersamples. The solid line marks the line where C₁≡[log 2(hsa-miR-9*)+log2(hsa-miR-92b)]=19, and provides perfect separation between brainprimary tumors and other samples, including other primary tumors andmetastases to the brain. The dashed lines mark a confidence range offactor 2 above or below, C₁=20 (upper line) and C₁=18 (lower line). Only2 of the samples (<1%) fall within the low-confidence range.

FIG. 5 demonstrates the expression levels of microRNA as detected bymicroarray in 15 primary brain tumors (squares), 187 primary tumors fromother tissues (diamonds), and 50 brain metastases from various tissueorigins (circles). A) Hsa-miR-128a (SEQ ID NO: 28) and hsa-miR-128b (SEQID NO: 30) have highly correlated expression values. These microRNAs arehigh in brain primary tumors, low in other primary tumors, andintermediate in brain metastasis samples. B) In contrast tohsa-miR-128a, hsa-miR-92b (SEQ ID NO: 14) is specifically expressed inbrain primary tumors, and is lower in primary tumors from other tissuesand in their brain metastases.

FIG. 6 demonstrates the identification of metastatic brain tumors usingmicroRNA qRT-PCR data. A) Expression levels (50-C_(t)) of hsa-miR-124(SEQ ID NO: 16) and hsa-miR-9 (SEQ ID NO: 27) in 16 brain primary tumors(squares), 15 primary tumors from other tissues (diamonds) and 16 brainmetastases originating from various tissues (circles). Expression levelsof hsa-miR-124 and hsa-miR-9 are higher in brain primary tumors comparedto primary tumors from other tissues. The expression levels ofhsa-miR-124 in metastatic samples span a wide range and are more similarto brain primary tumors; the expression levels of hsa-miR-9 inmetastatic samples are more similar to the non-brain primary tumors. B)Expression levels (50-C_(t)) of hsa-miR-9* (SEQ ID NO: 20) andhsa-miR-92b (SEQ ID NO: 14) in the same samples. Expression levels ofthese microRNAs are high in brain primary tumors and lower in all othersamples. The solid line marksC^(RT*)100-[C_(t)(hsa-miR-9*)+C_(t)(hsa-miR-92b)]=39.9, a thresholdwhich was fit to the training set half of the data. The test-set samples(dark squares/circles/diamonds) were accurately classified by thisthreshold, with one outlier. Data points with C_(t) larger than 40 areshown with C_(t)=40, at (50-C_(t))=10.

DETAILED DESCRIPTION OF THE INVENTION

The invention is based on the discovery that specific nucleic acids (SEQID NOS: 1-44) may be used for the classification of cancers. The presentinvention provides a sensitive, specific and accurate method which maybe used to distinguish between different tumor origins and betweenprimary and metastatic malignancies.

Metastatic tumors account for the overwhelming majority of all hepaticmalignancies in the non cirrhotic liver. In the cirrhotic liver,however, primary hepatic malignancies are more common than metastatictumors. Carcinomas of the lung, breast, colon and pancreas are the mostcommon primary sites in adults. The distinction between primary andmetastatic malignancy in the liver is of both therapeutic and prognosticsignificance.

In many cases the differential diagnosis of liver tumor includeshepatocellular carcinoma versus metastatic tumor. Some metastases, asfrom renal origin, can mimic hepatocellular carcinoma, and metastatictumor may invade liver-cell plates, giving a false impression of primarycarcinoma arising within them. Primary extrahepatic carcinomas of thestomach and colon and carcinoma of sex cord-stromal tumors of the ovarymay closely resemble HCC in both their morphology and immunoexpressionof CEA and alfafetoprotein.

Metastatic tumors tend to recapitulate their appearance in the primaryorgan and specific tumor types generally maintain consistent cytologicappearance. Adenocarcinoma, although frequently recognizable as anentity, presents the greatest difficulty for those attempting to make aspecific diagnosis as to site of origin.

According to the present invention, the ability of microRNA biomarkersto differentiate HCC from metastatic liver tumors of various origins wasdemonstrated. The expression of human microRNAs in 144 hepatic andnon-hepatic tumors was examined by microRNA microarray. The expressionof hsa-miR-141 (SEQ ID NO: 6) and hsa-miR-200c (SEQ ID NO: 8) wassignificantly higher in non-hepatic primary tumors compared to HCC(p-value<10̂-9 for each) and allows highly accurate differentialdiagnosis of hepatocellular carcinoma from metastatic adenocarcinoma(sensitivity=98%; specificity=93%). Similar results were obtained byusing the combination of hsa-miR-200a (SEQ ID NO: 10) and hsa-miR-200b(SEQ ID NO: 12).

hsa-miR-141, hsa-miR-200a, hsa-miR-200b and hsa-miR-200c are part of onepredicted polycistronic pri-microRNA, in an intronic region of atranscription unit (EST with no ORF) on Chr.12p13.31 (Landgraf, P., etal., Cell, 2007. 129(7): p. 1401-14).

Hsa-miR-194 (SEQ ID NO: 1) and hsa-miR-205 (SEQ ID NO: 4) weredifferentially expressed in primary GI and non-GI tumors (e.g., lung orbreast adenocarcinomas) (p-value<0.01 for hsa-miR-205 and p-value<10̂-11for hsa-miR-194), and may serve as potent biomarkers for theidentification of tumors of GI origin.

The combination of another pair of microRNAs, hsa-miR-92b (SEQ ID NO:14) and hsa-miR-9* (SEQ ID NO: 20), was capable of identifying primarybrain tumors (p-value<2.5e-4 for each compared to normal brain,p-value<5e-25 for each compared to other primary and metastatic tumors).The combination of another pair of microRNAs, hsa-miR-124a (SEQ ID NO:16) and hsa-miR-219 (SEQ ID NO: 24), was capable of differentiatingnormal brain and primary brain tumors from other primary tumors(p-value<2e-44 for each) or from brain metastases (p-value<6e-6 foreach).

The present invention provides diagnostic assays and methods, bothquantitative and qualitative for detecting, diagnosing, monitoring,staging and prognosticating cancers by comparing levels of the specificmicroRNA molecules of the invention. Such levels are preferably measuredin at least one of biopsies, tumor samples, cells, tissues and/or bodilyfluids, including determination of normal and abnormal levels. Thepresent invention provides methods for diagnosing the presence of aspecific cancer by analyzing for changes in levels of said microRNAmolecules in biopsies, tumor samples, cells, tissues or bodily fluids.

In the present invention, determining the presence of said microRNAlevels in biopsies, tumor samples, cells, tissues or bodily fluid, isparticularly useful for discriminating between different cancers.

All the methods of the present invention may optionally includemeasuring levels of other cancer markers. Other cancer markers, inaddition to said microRNA molecules, useful in the present inventionwill depend on the cancer being tested and are known to those of skillin the art.

Assay techniques that can be used to determine levels of geneexpression, such as the nucleic acid sequence of the present invention,in a sample derived from a patient are well known to those of skill inthe art. Such assay methods include, without limitation,radioimmunoassays, reverse transcriptase PCR (RT-PCR) assays,immunohistochemistry assays, in situ hybridization assays,competitive-binding assays, Northern Blot analyses, ELISA assays andbiochip analysis.

In some embodiments of the invention, correlations and/or hierarchicalclustering can be used to assess the similarity of the expression levelof the nucleic acid sequences of the invention between a specific sampleand different exemplars of cancer samples, by setting an arbitrarythreshold for assigning a sample or cancer sample to one of two groups.Alternatively, in a preferred embodiment, the threshold for assignmentis treated as a parameter, which can be used to quantify the confidencewith which samples are assigned to each class. The threshold forassignment can be scaled to favor sensitivity or specificity, dependingon the clinical scenario. The correlation value to the reference datagenerates a continuous score that can be scaled.

DEFINITIONS

Before the present compositions and methods are disclosed and described,it is to be understood that the terminology used herein is for thepurpose of describing particular embodiments only and is not intended tobe limiting. It must be noted that, as used in the specification and theappended claims, the singular forms “a,” “an” and “the” include pluralreferents unless the context clearly dictates otherwise.

Aberrant Proliferation

As used herein, the term “aberrant proliferation” means cellproliferation that deviates from the normal, proper, or expected course.For example, aberrant cell proliferation may include inappropriateproliferation of cells whose DNA or other cellular components havebecome damaged or defective. Aberrant cell proliferation may includecell proliferation whose characteristics are associated with anindication caused by, mediated by, or resulting in inappropriately highlevels of cell division, inappropriately low levels of apoptosis, orboth. Such indications may be characterized, for example, by single ormultiple local abnormal proliferations of cells, groups of cells, ortissue(s), whether cancerous or non-cancerous, benign or malignant.

About

As used herein, the term “about” refers to +/−10%.

Attached

“Attached” or “immobilized” as used herein to refer to a probe and asolid support may mean that the binding between the probe and the solidsupport is sufficient to be stable under conditions of binding, washing,analysis, and removal. The binding may be covalent or non-covalent.Covalent bonds may be formed directly between the probe and the solidsupport or may be formed by a cross linker or by inclusion of a specificreactive group on either the solid support or the probe or bothmolecules. Non-covalent binding may be one or more of electrostatic,hydrophilic, and hydrophobic interactions. Included in non-covalentbinding is the covalent attachment of a molecule, such as streptavidin,to the support and the non-covalent binding of a biotinylated probe tothe streptavidin. Immobilization may also involve a combination ofcovalent and non-covalent interactions.

Biological Sample

“Biological sample” as used herein may mean a sample of biologicaltissue or fluid that comprises nucleic acids. Such samples include, butare not limited to, tissue or fluid isolated from subjects. Biologicalsamples may also include sections of tissues such as biopsy and autopsysamples, frozen sections taken for histologic purposes, blood, plasma,serum, sputum, stool, tears, mucus, hair, and skin. Biological samplesalso include explants and primary and/or transformed cell culturesderived from animal or patient tissues. Biological samples may also beblood, a blood fraction, urine, effusions, ascitic fluid, amnioticfluid, saliva, cerebrospinal fluid, cervical secretions, vaginalsecretions, endometrial secretions, gastrointestinal secretions,bronchial secretions, sputum, cell line, tissue sample, or secretionsfrom the breast. A biological sample may be provided by removing asample of cells from a subject but can also be accomplished by usingpreviously isolated cells (e.g., isolated by another person, at anothertime, and/or for another purpose), or by performing the methodsdescribed herein in vivo. Archival tissues, such as those havingtreatment or outcome history, may also be used.

Cancer

The term “cancer” is meant to include all types of cancerous growths oroncogenic processes, metastatic tissues or malignantly transformedcells, tissues, or organs, irrespective of histopathologic type or stageof invasiveness. Examples of cancers include but are nor limited tosolid tumors and leukemias, including: apudoma, choristoma, branchioma,malignant carcinoid syndrome, carcinoid heart disease, carcinoma (e.g.,Walker, basal cell, basosquamous, Brown-Pearce, ductal, Ehrlich tumor,non-small cell lung, oat cell, papillary, bronchiolar, bronchogenic,squamous cell, and transitional cell), histiocytic disorders, leukemia(e.g., B cell, mixed cell, null cell, T cell, T-cell chronic,HTLV-II-associated, lymphocytic acute, lymphocytic chronic, mast cell,and myeloid), histiocytosis malignant, Hodgkin disease,immunoproliferative small, non-Hodgkin lymphoma, plasmacytoma,reticuloendotheliosis, melanoma, chondroblastoma, chondroma,chondrosarcoma, fibroma, fibrosarcoma, giant cell tumors, histiocytoma,lipoma, liposarcoma, mesothelioma, myxoma, myxosarcoma, osteoma,osteosarcoma, Ewing sarcoma, synovioma, adenofibroma, adenolymphoma,carcinosarcoma, chordoma, craniopharyngioma, dysgerminoma, hamartoma,mesenchymoma, mesonephroma, myosarcoma, ameloblastoma, cementoma,odontoma, teratoma, thymoma, trophoblastic tumor, adeno-carcinoma,adenoma, cholangioma, cholesteatoma, cylindroma, cystadenocarcinoma,cystadenoma, granulosa cell tumor, gynandroblastoma, hepatoma,hidradenoma, islet cell tumor, Leydig cell tumor, papilloma, Sertolicell tumor, theca cell tumor, leiomyoma, leiomyosarcoma, myoblastoma,myosarcoma, rhabdomyoma, rhabdomyosarcoma, ependymoma, ganglioneuroma,glioma, medulloblastoma, meningioma, neurilemmoma, neuroblastoma,neuroepithelioma, neurofibroma, neuroma, paraganglioma, paragangliomanonchromaffin, angiokeratoma, angiolymphoid hyperplasia witheosinophilia, angioma sclerosing, angiomatosis, glomangioma,hemangioendothelioma, hemangioma, hemangiopericytoma, hemangiosarcoma,lymphangioma, lymphangiomyoma, lymphangiosarcoma, pinealoma,carcinosarcoma, chondrosarcoma, cystosarcoma, phyllodes, fibrosarcoma,hemangiosarcoma, leimyosarcoma, leukosarcoma, liposarcoma,lymphangiosarcoma, myosarcoma, myxosarcoma, ovarian carcinoma,rhabdomyosarcoma, sarcoma (e.g., Ewing, experimental, Kaposi, and mastcell), neurofibromatosis, and cervical dysplasia, and other conditionsin which cells have become immortalized or transformed.

Classification

The term classification refers to a procedure and/or algorithm in whichindividual items are placed into groups or classes based on quantitativeinformation on one or more characteristics inherent in the items(referred to as traits, variables, characters, features, etc) and basedon a statistical model and/or a training set of previously labeleditems. A “classification tree” is a decision tree that placescategorical variables into classes.

Ct

Ct signals represent the first cycle of PCR where amplification crossesa threshold (cycle threshold) of fluorescence. Accordingly, low valuesof Ct represent high abundance or expression levels of the microRNA.

In some embodiments the PCR Ct signal is normalized such that thenormalized Ct remains inversed from the expression level. In otherembodiments the PCR Ct signal may be normalized and then inverted suchthat low normalized-inverted Ct represents low abundance or expressionlevels of the microRNA.

Complement

“Complement” or “complementary” as used herein to refer to a nucleicacid may mean Watson-Crick (e.g., A-T/U and C-G) or Hoogsteen basepairing between nucleotides or nucleotide analogs of nucleic acidmolecules. A full complement or fully complementary may mean 100%complementary base pairing between nucleotides or nucleotide analogs ofnucleic acid molecules.

Data Processing Routine

As used herein, a “data processing routine” refers to a process that canbe embodied in software that determines the biological significance ofacquired data (i.e., the ultimate results of an assay or analysis). Forexample, the data processing routine can make determination of tissue oforigin based upon the data collected. In the systems and methods herein,the data processing routine can also control the data collection routinebased upon the results determined. The data processing routine and thedata collection routines can be integrated and provide feedback tooperate the data acquisition, and hence provide assay-based judgingmethods.

Data Set

As use herein, the term “data set” refers to numerical values obtainedfrom the analysis, These numerical values associated with analysis maybe values such as peak height and area under the curve.

Data Structure

As used herein the term “data structure” refers to a combination of twoor more data sets, applying one or more mathematical manipulations toone or more data sets to obtain one or more new data sets, ormanipulating two or more data sets into a form that provides a visualillustration of the data in a new way. An example of a data structureprepared from manipulation of two or more data sets would be ahierarchical cluster.

Detection

“Detection” means detecting the presence of a component in a sample.Detection also means detecting the absence of a component. Detectionalso means measuring the level of a component, either quantitatively orqualitatively.

Differential Expression

“Differential expression” means qualitative or quantitative differencesin the temporal and/or cellular gene expression patterns within andamong cells and tissue. Thus, a differentially expressed gene mayqualitatively have its expression altered, including an activation orinactivation, in, e.g., normal versus disease tissue. Genes may beturned on or turned off in a particular state, relative to another statethus permitting comparison of two or more states. A qualitativelyregulated gene may exhibit an expression pattern within a state or celltype which may be detectable by standard techniques. Some genes may beexpressed in one state or cell type, but not in both. Alternatively, thedifference in expression may be quantitative, e.g., in that expressionis modulated, either up-regulated, resulting in an increased amount oftranscript, or down-regulated, resulting in a decreased amount oftranscript. The degree to which expression differs need only be largeenough to quantify via standard characterization techniques such asexpression arrays, quantitative reverse transcriptase PCR, northernanalysis, real-time PCR, in situ hybridization and RNase protection.

Expression Profile

The term “expression profile” is used broadly to include a genomicexpression profile, e.g., an expression profile of microRNAs. Profilesmay be generated by any convenient means for determining a level of anucleic acid sequence e.g. quantitative hybridization of microRNA,labeled microRNA, amplified microRNA, cRNA, etc., quantitative PCR,ELISA for quantitation, and the like, and allow the analysis ofdifferential gene expression between two samples. A subject or patienttumor sample, e.g., cells or collections thereof, e.g., tissues, isassayed. Samples are collected by any convenient method, as known in theart. Nucleic acid sequences of interest are nucleic acid sequences thatare found to be predictive, including the nucleic acid sequencesprovided above, where the expression profile may include expression datafor 5, 10, 20, 25, 50, 100 or more of, including all of the listednucleic acid sequences. According to some embodiments, the term“expression profile” means measuring the abundance of the nucleic acidsequences in the measured samples.

Expression Ratio

“Expression ratio” as used herein refers to relative expression levelsof two or more nucleic acids as determined by detecting the relativeexpression levels of the corresponding nucleic acids in a biologicalsample.

FDR

When performing multiple statistical tests, for example in comparing thesignal between two groups in multiple data features, there is anincreasingly high probability of obtaining false positive results, byrandom differences between the groups that can reach levels that wouldotherwise be considered as statistically significant. In order to limitthe proportion of such false discoveries, statistical significance isdefined only for data features in which the differences reached ap-value (by two-sided t-test) below a threshold, which is dependent onthe number of tests performed and the distribution of p-values obtainedin these tests.

Fragment

“Fragment” is used herein to indicate a non-full length part of anucleic acid. Thus, a fragment is itself also a nucleic acid.

Gene

“Gene” used herein may be a natural (e.g., genomic) or synthetic genecomprising transcriptional and/or translational regulatory sequencesand/or a coding region and/or non-translated sequences (e.g., introns,5′- and 3′-untranslated sequences). The coding region of a gene may be anucleotide sequence coding for an amino acid sequence or a functionalRNA, such as tRNA, rRNA, catalytic RNA, siRNA, miRNA or antisense RNA. Agene may also be an mRNA or cDNA corresponding to the coding regions(e.g., exons and miRNA) optionally comprising 5′- or 3′-untranslatedsequences linked thereto. A gene may also be an amplified nucleic acidmolecule produced in vitro comprising all or a part of the coding regionand/or 5′- or 3′-untranslated sequences linked thereto.

Groove Binder/Minor Groove Binder (MGB)

“Groove binder” and/or “minor groove binder” may be used interchangeablyand refer to small molecules that fit into the minor groove ofdouble-stranded DNA, typically in a sequence-specific manner. Minorgroove binders may be long, flat molecules that can adopt acrescent-like shape and thus, fit snugly into the minor groove of adouble helix, often displacing water. Minor groove binding molecules maytypically comprise several aromatic rings connected by bonds withtorsional freedom such as furan, benzene, or pyrrole rings. Minor groovebinders may be antibiotics such as netropsin, distamycin, berenil,pentamidine and other aromatic diamidines, Hoechst 33258, SN 6999,aureolic anti-tumor drugs such as chromomycin and mithramycin, CC-1065,dihydrocyclopyrroloindole tripeptide (DPI₃),1,2-dihydro-(3H)-pyrrolo[3,2-e]indole-7-carboxylate (CDPI₃), and relatedcompounds and analogues, including those described in Nucleic Acids inChemistry and Biology, 2d ed., Blackburn and Gait, eds., OxfordUniversity Press, 1996, and PCT Published Application No. WO 03/078450,the contents of which are incorporated herein by reference. A minorgroove binder may be a component of a primer, a probe, a hybridizationtag complement, or combinations thereof. Minor groove binders mayincrease the T_(m) of the primer or a probe to which they are attached,allowing such primers or probes to effectively hybridize at highertemperatures.

Host Cell

“Host cell” used herein may be a naturally occurring cell or atransformed cell that may contain a vector and may support replicationof the vector. Host cells may be cultured cells, explants, cells invivo, and the like. Host cells may be prokaryotic cells such as E. coli,or eukaryotic cells such as yeast, insect, amphibian, or mammaliancells, such as CHO and HeLa.

Identity

“Identical” or “identity” as used herein in the context of two or morenucleic acids or polypeptide sequences may mean that the sequences havea specified percentage of residues that are the same over a specifiedregion. The percentage may be calculated by optimally aligning the twosequences, comparing the two sequences over the specified region,determining the number of positions at which the identical residueoccurs in both sequences to yield the number of matched positions,dividing the number of matched positions by the total number ofpositions in the specified region, and multiplying the result by 100 toyield the percentage of sequence identity. In cases where the twosequences are of different lengths or the alignment produces one or morestaggered ends and the specified region of comparison includes only asingle sequence, the residues of single sequence are included in thedenominator but not the numerator of the calculation. When comparing DNAand RNA, thymine (T) and uracil (U) may be considered equivalent.Identity may be performed manually or by using a computer sequencealgorithm such as BLAST or BLAST 2.0.

In Situ Detection

“In situ detection” as used herein means the detection of expression orexpression levels in the original site hereby meaning in a tissue samplesuch as biopsy.

k-Nearest Neighbor

The phrase “k-nearest neighbor” refers to a classification method thatclassifies a point by calculating the distances between the point andpoints in the training data set. Then it assigns the point to the classthat is most common among its k-nearest neighbors (where k is aninteger).

Label

“Label” as used herein may mean a composition detectable byspectroscopic, photochemical, biochemical, immunochemical, chemical, orother physical means. For example, useful labels include ³²P,fluorescent dyes, electron-dense reagents, enzymes (e.g., as commonlyused in an ELISA), biotin, digoxigenin, or haptens and other entitieswhich can be made detectable. A label may be incorporated into nucleicacids and proteins at any position.

Liver Cancer

“Liver cancer” means malignancy of the liver, either a primary cancer ormetastasized cancer. In certain embodiments, liver cancer includes, butis not limited to, cancer arising from hepatocytes, such as, forexample, hepatomas and hepatocellular carcinomas; fibrolamellar; andcholangiocarcinomas (or bile duct cancer).

Logistic Regression

Logistic regression is part of a category of statistical models calledgeneralized linear models. Logistic regression can allows one to predicta discrete outcome, such as group membership, from a set of variablesthat may be continuous, discrete, dichotomous, or a mix of any of these.The dependent or response variable can be dichotomous, for example, oneof two possible types of cancer. Logistic regression models the naturallog of the odds ratio, i.e. the ratio of the probability of belonging tothe first group (P) over the probability of belonging to the secondgroup (1-P), as a linear combination of the different expression levels(in log-space). The logistic regression output can be used as aclassifier by prescribing that a case or sample will be classified intothe first type is P is greater than 0.5 or 50%. Alternatively, thecalculated probability P can be used as a variable in other contextssuch as a 1D or 2D threshold classifier.

1D/2D Threshold Classifier

“1D/2D threshold classifier” used herein may mean an algorithm forclassifying a case or sample such as a cancer sample into one of twopossible types such as two types of cancer. For a 1D thresholdclassifier, the decision is based on one variable and one predeterminedthreshold value; the sample is assigned to one class if the variableexceeds the threshold and to the other class if the variable is lessthan the threshold. A 2D threshold classifier is an algorithm forclassifying into one of two types based on the values of two variables.A threshold may be calculated as a function (usually a continuous oreven a monotonic function) of the first variable; the decision is thenreached by comparing the second variable to the calculated threshold,similar to the 1D threshold classifier.

Metastasis

“Metastasis” means the process by which cancer spreads from the place atwhich it first arose as a primary tumor to other locations in the body.The metastatic progression of a primary tumor reflects multiple stages,including dissociation from neighboring primary tumor cells, survival inthe circulation, and growth in a secondary location.

Nucleic Acid

“Nucleic acid” or “oligonucleotide” or “polynucleotide” used herein maymean at least two nucleotides covalently linked together. The depictionof a single strand also defines the sequence of the complementarystrand. Thus, a nucleic acid also encompasses the complementary strandof a depicted single strand. Many variants of a nucleic acid may be usedfor the same purpose as a given nucleic acid. Thus, a nucleic acid alsoencompasses substantially identical nucleic acids and complementsthereof. A single strand provides a probe that may hybridize to a targetsequence under stringent hybridization conditions. Thus, a nucleic acidalso encompasses a probe that hybridizes under stringent hybridizationconditions.

Nucleic acids may be single stranded or double stranded, or may containportions of both double stranded and single stranded sequence. Thenucleic acid may be DNA, both genomic and cDNA, RNA, or a hybrid, wherethe nucleic acid may contain combinations of deoxyribo- andribo-nucleotides, and combinations of bases including uracil, adenine,thymine, cytosine, guanine, inosine, xanthine hypoxanthine, isocytosineand isoguanine Nucleic acids may be obtained by chemical synthesismethods or by recombinant methods.

A nucleic acid will generally contain phosphodiester bonds, althoughnucleic acid analogs may be included that may have at least onedifferent linkage, e.g., phosphoramidate, phosphorothioate,phosphorodithioate, or O-methylphosphoroamidite linkages and peptidenucleic acid backbones and linkages. Other analog nucleic acids includethose with positive backbones; non-ionic backbones, and non-ribosebackbones, including those described in U.S. Pat. Nos. 5,235,033 and5,034,506, which are incorporated by reference. Nucleic acids containingone or more non-naturally occurring or modified nucleotides are alsoincluded within one definition of nucleic acids. The modified nucleotideanalog may be located for example at the 5′-end and/or the 3′-end of thenucleic acid molecule. Representative examples of nucleotide analogs maybe selected from sugar- or backbone-modified ribonucleotides. It shouldbe noted, however, that also nucleobase-modified ribonucleotides, i.e.ribonucleotides, containing a non-naturally occurring nucleobase insteadof a naturally occurring nucleobase such as uridines or cytidinesmodified at the 5-position, e.g. 5-(2-amino)propyl uridine, 5-bromouridine; adenosines and guanosines modified at the 8-position, e.g.8-bromo guanosine; deaza nucleotides, e.g. 7-deaza-adenosine; O- andN-alkylated nucleotides, e.g. N6-methyl adenosine are suitable. The2′-OH-group may be replaced by a group selected from H, OR, R, halo, SH,SR, NH₂, NHR, NR₂ or CN, wherein R is C₁-C₆ alkyl, alkenyl or alkynyland halo is F, Cl, Br or I. Modified nucleotides also includenucleotides conjugated with cholesterol through, e.g., a hydroxyprolinollinkage as described in Krutzfeldt et al., Nature 438:685-689 (2005),Soutschek et al., Nature 432:173-178 (2004), and U.S. Patent PublicationNo. 20050107325, which are incorporated herein by reference. Additionalmodified nucleotides and nucleic acids are described in U.S. PatentPublication No. 20050182005, which is incorporated herein by reference.Modifications of the ribose-phosphate backbone may be done for a varietyof reasons, e.g., to increase the stability and half-life of suchmolecules in physiological environments, to enhance diffusion acrosscell membranes, or as probes on a biochip. The backbone modification mayalso enhance resistance to degradation, such as in the harsh endocyticenvironment of cells. The backbone modification may also reduce nucleicacid clearance by hepatocytes, such as in the liver and kidney. Mixturesof naturally occurring nucleic acids and analogs may be made;alternatively, mixtures of different nucleic acid analogs, and mixturesof naturally occurring nucleic acids and analogs may be made.

Probe

“Probe” as used herein may mean an oligonucleotide capable of binding toa target nucleic acid of complementary sequence through one or moretypes of chemical bonds, usually through complementary base pairing,usually through hydrogen bond formation. Probes may bind targetsequences lacking complete complementarity with the probe sequencedepending upon the stringency of the hybridization conditions. There maybe any number of base pair mismatches which will interfere withhybridization between the target sequence and the single strandednucleic acids described herein. However, if the number of mutations isso great that no hybridization can occur under even the least stringentof hybridization conditions, the sequence is not a complementary targetsequence. A probe may be single stranded or partially single andpartially double stranded. The strandedness of the probe is dictated bythe structure, composition, and properties of the target sequence.Probes may be directly labeled or indirectly labeled such as with biotinto which a streptavidin complex may later bind.

Reference Expression Profile

As used herein, the phrase “reference expression profile” refers to acriterion expression value to which measured values are compared inorder to determine the detection of a subject with lung cancer. Thereference expression profile may be based on the abundance of thenucleic acids, or may be based on a combined metric score thereof.

Sensitivity

“sensitivity” used herein may mean a statistical measure of how well abinary classification test correctly identifies a condition, for examplehow frequently it correctly classifies a cancer into the correct typeout of two possible types. The sensitivity for class A is the proportionof cases that are determined to belong to class “A” by the test out ofthe cases that are in class “A”, as determined by some absolute or goldstandard.

Specificity

“specificity” used herein may mean a statistical measure of how well abinary classification test correctly identifies a condition, for examplehow frequently it correctly classifies a cancer into the correct typeout of two possible types. The sensitivity for class A is the proportionof cases that are determined to belong to class “not A” by the test outof the cases that are in class “not A”, as determined by some absoluteor gold standard.

Stringent Hybridization Conditions

“Stringent hybridization conditions” used herein may mean conditionsunder which a first nucleic acid sequence (e.g., probe) will hybridizeto a second nucleic acid sequence (e.g., target), such as in a complexmixture of nucleic acids. Stringent conditions are sequence-dependentand will be different in different circumstances. Stringent conditionsmay be selected to be about 5-10° C. lower than the thermal meltingpoint (T_(m)) for the specific sequence at a defined ionic strength pH.The T_(m) may be the temperature (under defined ionic strength, pH, andnucleic concentration) at which 50% of the probes complementary to thetarget hybridize to the target sequence at equilibrium (as the targetsequences are present in excess, at T_(m), 50% of the probes areoccupied at equilibrium). Stringent conditions may be those in which thesalt concentration is less than about 1.0 M sodium ion, such as about0.01-1.0 M sodium ion concentration (or other salts) at pH 7.0 to 8.3and the temperature is at least about 30° C. for short probes (e.g.,about 10-50 nucleotides) and at least about 60° C. for long probes(e.g., greater than about 50 nucleotides). Stringent conditions may alsobe achieved with the addition of destabilizing agents such as formamide.For selective or specific hybridization, a positive signal may be atleast 2 to 10 times background hybridization. Exemplary stringenthybridization conditions include the following: 50% formamide, 5×SSC,and 1% SDS, incubating at 42° C., or, 5×SSC, 1% SDS, incubating at 65°C., with wash in 0.2×SSC, and 0.1% SDS at 65° C.

Substantially Complementary

“Substantially complementary” used herein may mean that a first sequenceis at least 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 97%, 98% or 99%identical to the complement of a second sequence over a region of 8, 9,10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 30, 35,40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100 or more nucleotides,or that the two sequences hybridize under stringent hybridizationconditions.

Substantially Identical

“Substantially identical” used herein may mean that a first and secondsequence are at least 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 97%, 98%or 99% identical over a region of 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,18, 19, 20, 21, 22, 23, 24, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75,80, 85, 90, 95, 100 or more nucleotides or amino acids, or with respectto nucleic acids, if the first sequence is substantially complementaryto the complement of the second sequence.

Subject

As used herein, the term “subject” refers to a mammal, including bothhuman and other mammals. The methods of the present invention arepreferably applied to human subjects.

Target “Target” as used herein may mean a polynucleotide that may bebound by one or more probes under stringent hybridization conditions.

Threshold Expression Profile

As used herein, the phrase “threshold expression profile” refers to acriterion expression profile to which measured values are compared inorder to classify a cancer.

Tissue Sample

As used herein, a tissue sample is tissue obtained from a tissue biopsyusing methods well known to those of ordinary skill in the relatedmedical arts. The phrase “suspected of being cancerous” as used hereinmeans a cancer tissue sample believed by one of ordinary skill in themedical arts to contain cancerous cells. Methods for obtaining thesample from the biopsy include gross apportioning of a mass,microdissection, laser-based microdissection, or other art-knowncell-separation methods.

Tumor

“Tumor” as used herein, refers to all neoplastic cell growth andproliferation, whether malignant or benign, and all pre-cancerous andcancerous cells and tissues.

Variant

“Variant” used herein to refer to a nucleic acid may mean (i) a portionof a referenced nucleotide sequence; (ii) the complement of a referencednucleotide sequence or portion thereof; (iii) a nucleic acid that issubstantially identical to a referenced nucleic acid or the complementthereof; or (iv) a nucleic acid that hybridizes under stringentconditions to the referenced nucleic acid, complement thereof, or asequence substantially identical thereto.

Wild Type

As used herein, the term “wild type” sequence refers to a coding,non-coding or interface sequence is an allelic form of sequence thatperforms the natural or normal function for that sequence. Wild typesequences include multiple allelic forms of a cognate sequence, forexample, multiple alleles of a wild type sequence may encode silent orconservative changes to the protein sequence that a coding sequenceencodes.

The present invention employs miRNAs and related nucleic acids for theidentification, classification and diagnosis of specific cancers.

microRNA Processing

A gene coding for a miRNA may be transcribed leading to production of amiRNA primary transcript known as the pri-miRNA. The pri-miRNA maycomprise a hairpin with a stem and loop. The stem of the hairpin maycomprise mismatched bases. The pri-miRNA may comprise several hairpinsin a polycistronic structure.

The hairpin structure of the pri-miRNA may be recognized by Drosha,which is an RNase III endonuclease. Drosha may recognize terminal loopsin the pri-miRNA and cleave approximately two helical turns into thestem to produce a 60-70 nt precursor known as the pre-miRNA. Drosha maycleave the pri-miRNA with a staggered cut typical of RNase IIIendonucleases yielding a pre-miRNA stem loop with a 5′ phosphate and ˜2nucleotide 3′ overhang. Approximately one helical turn of stem (−10nucleotides) extending beyond the Drosha cleavage site may be essentialfor efficient processing. The pre-miRNA may then be actively transportedfrom the nucleus to the cytoplasm by Ran-GTP and the export receptorEx-portin-5.

The pre-miRNA may be recognized by Dicer, which is also an RNase IIIendonuclease. Dicer may recognize the double-stranded stem of thepre-miRNA. Dicer may also off the terminal loop two helical turns awayfrom the base of the stem loop leaving an additional 5′ phosphate and ˜2nucleotide 3′ overhang. The resulting siRNA-like duplex, which maycomprise mismatches, comprises the mature miRNA and a similar-sizedfragment known as the miRNA*. The miRNA and miRNA* may be derived fromopposing arms of the pri-miRNA and pre-miRNA. mRNA* sequences may befound in libraries of cloned miRNAs but typically at lower frequencythan the miRNAs.

Although initially present as a double-stranded species with miRNA*, themiRNA may eventually become incorporated as a single-stranded RNA into aribonucleoprotein complex known as the RNA-induced silencing complex(RISC). Various proteins can form the RISC, which can lead tovariability in specificity for miRNA/miRNA* duplexes, binding site ofthe target gene, activity of miRNA (repress or activate), and whichstrand of the miRNA/miRNA* duplex is loaded in to the RISC.

When the miRNA strand of the miRNA:miRNA* duplex is loaded into theRISC, the miRNA* may be removed and degraded. The strand of themiRNA:miRNA* duplex that is loaded into the RISC may be the strand whose5′ end is less tightly paired. In cases where both ends of themiRNA:miRNA* have roughly equivalent 5′ pairing, both miRNA and miRNA*may have gene silencing activity.

The RISC may identify target nucleic acids based on high levels ofcomplementarity between the miRNA and the mRNA, especially bynucleotides 2-7 of the miRNA. Only one case has been reported in animalswhere the interaction between the miRNA and its target was along theentire length of the miRNA. This was shown for mir-196 and Hox B8 and itwas further shown that mir-196 mediates the cleavage of the Hox B8 mRNA(Yekta et al 2004, Science 304-594). Otherwise, such interactions areknown only in plants (Bartel & Bartel 2003, Plant Physiol 132-709).

A number of studies have looked at the base-pairing requirement betweenmiRNA and its mRNA target for achieving efficient inhibition oftranslation (reviewed by Bartel 2004, Cell 116-281). In mammalian cells,the first 8 nucleotides of the miRNA may be important (Doench & Sharp2004 GenesDev 2004-504). However, other parts of the microRNA may alsoparticipate in mRNA binding. Moreover, sufficient base pairing at the 3′can compensate for insufficient pairing at the 5′ (Brennecke et al, 2005PLoS 3-e85). Computation studies, analyzing miRNA binding on wholegenomes have suggested a specific role for bases 2-7 at the 5′ of themiRNA in target binding but the role of the first nucleotide, foundusually to be “A” was also recognized (Lewis et al 2005 Cell 120-15).Similarly, nucleotides 1-7 or 2-8 were used to identify and validatetargets by Krek et al (2005, Nat Genet 37-495).

The target sites in the mRNA may be in the 5′ UTR, the 3′ UTR or in thecoding region. Interestingly, multiple miRNAs may regulate the same mRNAtarget by recognizing the same or multiple sites. The presence ofmultiple miRNA binding sites in most genetically identified targets mayindicate that the cooperative action of multiple RISCs provides the mostefficient translational inhibition.

miRNAs may direct the RISC to downregulate gene expression by either oftwo mechanisms: mRNA cleavage or translational repression. The miRNA mayspecify cleavage of the mRNA if the mRNA has a certain degree ofcomplementarity to the miRNA. When a miRNA guides cleavage, the cut maybe between the nucleotides pairing to residues 10 and 11 of the miRNA.Alternatively, the miRNA may repress translation if the miRNA does nothave the requisite degree of complementarity to the miRNA. Translationalrepression may be more prevalent in animals since animals may have alower degree of complementarity between the miRNA and binding site.

It should be noted that there may be variability in the 5′ and 3′ endsof any pair of miRNA and miRNA*. This variability may be due tovariability in the enzymatic processing of Drosha and Dicer with respectto the site of cleavage. Variability at the 5′ and 3′ ends of miRNA andmiRNA* may also be due to mismatches in the stem structures of thepri-miRNA and pre-miRNA. The mismatches of the stem strands may lead toa population of different hairpin structures. Variability in the stemstructures may also lead to variability in the products of cleavage byDrosha and Dicer.

Nucleic Acid

Nucleic acids are provided herein. The nucleic acid may comprise thesequence of SEQ ID NOS: 1-44 or variants thereof. The variant may be acomplement of the referenced nucleotide sequence. The variant may alsobe a nucleotide sequence that is substantially identical to thereferenced nucleotide sequence or the complement thereof. The variantmay also be a nucleotide sequence which hybridizes under stringentconditions to the referenced nucleotide sequence, complements thereof,or nucleotide sequences substantially identical thereto.

The nucleic acid may have a length of from 10 to 250 nucleotides. Thenucleic acid may have a length of at least 10, 11, 12, 13, 14, 15, 16,17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 35, 40, 45, 50,60, 70, 80, 90, 100, 125, 150, 175, 200 or 250 nucleotides. The nucleicacid may be synthesized or expressed in a cell (in vitro or in vivo)using a synthetic gene described herein. The nucleic acid may besynthesized as a single strand molecule and hybridized to asubstantially complementary nucleic acid to form a duplex. The nucleicacid may be introduced to a cell, tissue or organ in a single- ordouble-stranded form or capable of being expressed by a synthetic geneusing methods well known to those skilled in the art, including asdescribed in U.S. Pat. No. 6,506,559 which is incorporated by reference.

Nucleic Acid Complexes

The nucleic acid may further comprise one or more of the following: apeptide, a protein, a RNA-DNA hybrid, an antibody, an antibody fragment,a Fab fragment, and an aptamer.

Pri-miRNA

The nucleic acid may comprise a sequence of a pri-miRNA or a variantthereof. The pri-miRNA sequence may comprise from 45-30,000,50-25,000,100-20,000, 1,000-1,500 or 80-100 nucleotides. The sequence ofthe pri-miRNA may comprise a pre-miRNA, miRNA and miRNA*, as set forthherein, and variants thereof. The sequence of the pri-miRNA may comprisethe sequence of SEQ ID NOS: 1-33 or variants thereof.

The pri-miRNA may comprise a hairpin structure. The hairpin may comprisea first and second nucleic acid sequence that are substantiallycomplimentary. The first and second nucleic acid sequence may be from37-50 nucleotides. The first and second nucleic acid sequence may beseparated by a third sequence of from 8-12 nucleotides. The hairpinstructure may have a free energy less than −25 Kcal/mole as calculatedby the Vienna algorithm with default parameters, as described inHofacker et al., Monatshefte f. Chemie 125: 167-188 (1994), the contentsof which are incorporated herein. The hairpin may comprise a terminalloop of 4-20, 8-12 or 10 nucleotides. The pri-miRNA may comprise atleast 19% adenosine nucleotides, at least 16% cytosine nucleotides, atleast 23% thymine nucleotides and at least 19% guanine nucleotides.

Pre-miRNA

The nucleic acid may also comprise a sequence of a pre-miRNA or avariant thereof. The pre-miRNA sequence may comprise from 45-90, 60-80or 60-70 nucleotides. The sequence of the pre-miRNA may comprise a miRNAand a miRNA* as set forth herein. The sequence of the pre-miRNA may alsobe that of a pri-miRNA excluding from 0-160 nucleotides from the 5′ and3′ ends of the pri-miRNA. The sequence of the pre-miRNA may comprise thesequence of SEQ ID NOS: 1-33 or variants thereof.

miRNA

The nucleic acid may also comprise a sequence of a miRNA (includingmiRNA*) or a variant thereof. The miRNA sequence may comprise from13-33, 18-24 or 21-23 nucleotides. The miRNA may also comprise a totalof at least 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20,21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38,39 or 40 nucleotides. The sequence of the miRNA may be the first 13-33nucleotides of the pre-miRNA. The sequence of the miRNA may also be thelast 13-33 nucleotides of the pre-miRNA. The sequence of the miRNA maycomprise the sequence of SEQ ID NOS: 1-33 or variants thereof.

Probes

A probe is also provided comprising a nucleic acid described herein.Probes may be used for screening and diagnostic methods, as outlinedbelow. The probe may be attached or immobilized to a solid substrate,such as a biochip.

The probe may have a length of from 8 to 500, 10 to 100 or 20 to 60nucleotides. The probe may also have a length of at least 8, 9, 10, 11,12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29,30, 35, 40, 45, 50, 60, 70, 80, 90, 100, 120, 140, 160, 180, 200, 220,240, 260, 280 or 300 nucleotides. The probe may further comprise alinker sequence of from 10-60 nucleotides. The probe may comprise anucleic acid that is complementary to a sequence selected from the groupconsisting of SEQ ID NOS: 1-27, 32-33; a fragment thereof, and asequence having at least about 80% identity thereto.

Biochip

A biochip is also provided. The biochip may comprise a solid substratecomprising an attached probe or plurality of probes described herein.The probes may be capable of hybridizing to a target sequence understringent hybridization conditions. The probes may be attached atspatially defined addresses on the substrate. More than one probe pertarget sequence may be used, with either overlapping probes or probes todifferent sections of a particular target sequence. The probes may becapable of hybridizing to target sequences associated with a singledisorder appreciated by those in the art. The probes may either besynthesized first, with subsequent attachment to the biochip, or may bedirectly synthesized on the biochip.

The solid substrate may be a material that may be modified to containdiscrete individual sites appropriate for the attachment or associationof the probes and is amenable to at least one detection method.Representative examples of substrates include glass and modified orfunctionalized glass, plastics (including acrylics, polystyrene andcopolymers of styrene and other materials, polypropylene, polyethylene,polybutylene, polyurethanes, TeflonJ, etc.), polysaccharides, nylon ornitrocellulose, resins, silica or silica-based materials includingsilicon and modified silicon, carbon, metals, inorganic glasses andplastics. The substrates may allow optical detection without appreciablyfluorescing.

The substrate may be planar, although other configurations of substratesmay be used as well. For example, probes may be placed on the insidesurface of a tube, for flow-through sample analysis to minimize samplevolume. Similarly, the substrate may be flexible, such as a flexiblefoam, including closed cell foams made of particular plastics.

The biochip and the probe may be derivatized with chemical functionalgroups for subsequent attachment of the two. For example, the biochipmay be derivatized with a chemical functional group including, but notlimited to, amino groups, carboxyl groups, oxo groups or thiol groups.Using these functional groups, the probes may be attached usingfunctional groups on the probes either directly or indirectly using alinker. The probes may be attached to the solid support by either the 5′terminus, 3′ terminus, or via an internal nucleotide.

The probe may also be attached to the solid support non-covalently. Forexample, biotinylated oligonucleotides can be made, which may bind tosurfaces covalently coated with streptavidin, resulting in attachment.Alternatively, probes may be synthesized on the surface using techniquessuch as photopolymerization and photolithography.

Diagnostic

As used herein the term “diagnosing” refers to classifying a pathologyor a symptom, determining a severity of the pathology (grade or stage),monitoring pathology progression, forecasting an outcome of a pathologyand/or prospects of recovery.

As used herein the phrase “subject in need thereof” refers to an animalor human subject who is known to have cancer, at risk of having cancer[e.g., a genetically predisposed subject, a subject with medical and/orfamily history of cancer, a subject who has been exposed to carcinogens,occupational hazard, environmental hazard] and/or a subject who exhibitssuspicious clinical signs of cancer [e.g., blood in the stool or melena,unexplained pain, sweating, unexplained fever, unexplained loss ofweight up to anorexia, changes in bowel habits (constipation and/ordiarrhea), tenesmus (sense of incomplete defecation, for rectal cancerspecifically), anemia and/or general weakness]. Additionally oralternatively, the subject in need thereof can be a healthy humansubject undergoing a routine well-being check up. According to someembodiments, the subject has a primary tumor. According to otherembodiments, the subject has metastatic cancer. According to anotherembodiment, the subject has cancer of unknown primary (CUP).

Analyzing presence of malignant or pre-malignant cells can be effectedin-vivo or ex-vivo, whereby a biological sample (e.g., biopsy) isretrieved. Such biopsy samples comprise cells and may be an incisionalor excisional biopsy. Alternatively the cells may be retrieved from acomplete resection.

While employing the present teachings, additional information may begleaned pertaining to the determination of treatment regimen, treatmentcourse and/or to the measurement of the severity of the disease.

As used herein the phrase “treatment regimen” refers to a treatment planthat specifies the type of treatment, dosage, schedule and/or durationof a treatment provided to a subject in need thereof (e.g., a subjectdiagnosed with a pathology). The selected treatment regimen can be anaggressive one which is expected to result in the best clinical outcome(e.g., complete cure of the pathology) or a more moderate one which mayrelieve symptoms of the pathology yet results in incomplete cure of thepathology. It will be appreciated that in certain cases the treatmentregimen may be associated with some discomfort to the subject or adverseside effects (e.g., a damage to healthy cells or tissue). The type oftreatment can include a surgical intervention (e.g., removal of lesion,diseased cells, tissue, or organ), a cell replacement therapy, anadministration of a therapeutic drug (e.g., receptor agonists,antagonists, hormones, chemotherapy agents) in a local or a systemicmode, an exposure to radiation therapy using an external source (e.g.,external beam) and/or an internal source (e.g., brachytherapy) and/orany combination thereof. The dosage, schedule and duration of treatmentcan vary, depending on the severity of pathology and the selected typeof treatment, and those of skills in the art are capable of adjustingthe type of treatment with the dosage, schedule and duration oftreatment.

A method of diagnosis is also provided. The method comprises detectingan expression level of a specific cancer-associated nucleic acid in abiological sample. The sample may be derived from a patient. Diagnosisof a specific cancer state in a patient may allow for prognosis andselection of therapeutic strategy. Further, the developmental stage ofcells may be classified by determining temporarily expressed specificcancer-associated nucleic acids.

In situ hybridization of labeled probes to tissue arrays may beperformed. When comparing the fingerprints between individual samplesthe skilled artisan can make a diagnosis, a prognosis, or a predictionbased on the findings. It is further understood that the nucleic acidsequence which indicate the diagnosis may differ from those whichindicate the prognosis and molecular profiling of the condition of thecells may lead to distinctions between responsive or refractoryconditions or may be predictive of outcomes.

Kits

A kit is also provided and may comprise a nucleic acid described hereintogether with any or all of the following: assay reagents, buffers,probes and/or primers, and sterile saline or another pharmaceuticallyacceptable emulsion and suspension base. In addition, the kits mayinclude instructional materials containing directions (e.g., protocols)for the practice of the methods described herein. The kit may furthercomprise a software package for data analysis of expression profiles.

For example, the kit may be a kit for the amplification, detection,identification or quantification of a target nucleic acid sequence. Thekit may comprise a poly(T) primer, a forward primer, a reverse primer,and a probe.

Detectable Malignancies

Brain cancer: Each year, approximately 15,000 cases of high gradeastrocytomas (glioblastoma multiforme) are diagnosed in the UnitedStates. The number is growing in both pediatric and adult populations.Standard treatments include cytoreductive surgery followed by radiationtherapy or chemotherapy. There is no cure, and virtually all patientsultimately succumb to recurrent or progressive disease. The overallsurvival for grade IV astrocytomas (glioblastoma multiforme) is poor,with 50% of patients dying in the first year after diagnosis.

According to the present invention, brain tumors were directly comparedto a wide range of epithelial tumors and metastases to the brain. Usingmicroarray data, it was found that elevated expression of just twomicroRNAs, hsa-miR-92b (SEQ ID NO: 14) and hsa-miR-9*(SEQ ID NO: 20), issufficient to distinguish brain primary tumors from tumors derived fromnon-brain tissues, and most significantly for diagnostic purposes, frommetastases located in the brain. This assay was translated to a qRT-PCRplatform, using additional samples as a training set to develop aclassifier. Validating on an independent set of test samples, it wasfound that the simple combination of hsa-miR-92b and hsa-miR-9 (SEQ IDNO: 27) (or hsa-miR-9*) can identify brain metastases from brain primarytumors with sensitivity of 88% and specificity of 100%. Thus, economicaland relatively easy evaluation of hsa-miR-92b and hsa-miR-9/9*expression, which can be performed robustly using either fresh frozen orfixed materials in the clinical setting, reveals whether neoplastictissue excised from the brain is brain-derived or represents ametastasis from another tissue. Taken together, the expression dataconcerning hsa-miR-92b and hsa-miR-9/9* suggest a connection betweenderegulation of microRNAs, pluripotency, and tumorigenesis.

Liver cancer: Primary liver cancer is the fifth most common cancerworldwide. Hepatocellular carcinoma (HCC) accounts for 80% of all livercancer and the rates of HCC have increased by over 70% in the last twodecades in the U.S. The fatality ratio (mortality/incidence) of livercancer is approximately 1, indicating that the majority of patients liveless than a year. Late diagnosis due to lack of clinical symptoms is oneof the main reasons for the high fatality ratio. Liver cancer can resultfrom both viral infection and chemical exposure. Known risk factorsinclude hepatitis B and C virus infection. It is not known whetherdistinct routes to liver cancer affect the same or different cellularpathways. No mutational model has yet been developed for liver cancer asit has been for other cancers. The molecular events that precedeneoplastic transformation of the liver are not well understood. With noclearly identified cause, successful treatment options are lacking.

Nearly any primary tumor site can deposit metastases in the liver, sincethe liver filters blood from throughout the body. Most discussionsrelated to the treatment of metastatic tumors in the liver focus onthose originating from the colon. In fact, the most common cause ofdeath from colorectal cancer is liver metastasis.

Up to 50% of liver metastases are of colorectal cancer origin, while theremainder metastasizes from a wide variety of primary cancer sitesincluding sarcomas, breast and kidney, as well as neuroendocrine tumors.

HCC may be solitary or multicentric, and it may mimic liver metastases.Furthermore hemangiomas and liver metastases are often confused inimaging methods. In general, the imaging appearances of liver metastasesare nonspecific, and biopsy specimens are required for histologicaldiagnosis. Various biochemical markers have been proposed to indicateliver metastases. However, the diagnostic accuracy of tumor markers hasnot yet been defined.

The following examples are presented in order to more fully illustratesome embodiments of the invention. They should, in no way be construed,however, as limiting the broad scope of the invention.

EXAMPLES Material and Methods 1. Tumor Samples

27 fresh frozen and 141 formalin-fixed paraffin embedded (FFPE) tumorsamples obtained from several sources (Sheba Medical Center,Tel-Hashomer, Israel; ABS Inc., Wilmington, Del.; Seoul NationalUniversity College of Medicine, Seoul, South Korea; Indivumed GmbH,Hamburg, Germany; Soroka University Medical Center, Beer-Sheva, Israel)were used for comparing liver tumors to non-liver tumors and livermetastases. 2 fresh frozen brain normal samples (obtained from AmbionInc.), 3 fresh-frozen liver tumor samples (obtained from Seoul NationalUniversity College of Medicine, Seoul, South Korea) and 285 FFPE tumorsamples (obtained from Sheba Medical Center, Tel-Hashomer, Israel;Soroka University Medical Center, Beer-Sheva, Israel; BeilinsonHospital, Rabin Medical Center, Petah-Tikva, Israel; ABS Inc.,Wilmington, Del.; Tel Aviv Sourasky Medical Center, Tel Aviv, Israel;Bnai Zion Medical Center, Haifa, Israel) were used for comparing braintumors to normal brain, non-brain tumors and brain metastases. The studyprotocol was approved by the Research Ethics Board of each of thecontributing institutes. Each of the FFPE samples was evaluated by apathologist for histological type, grade and tumor percentages based onhematoxilin-eosin (H&E) stained slides, performed on the first and/orlast sections of the sample. The tumor content was ≧50% in 85% of theFFPE samples. For frozen samples, information was extracted from medicalrecords.

252 of the samples were profiled by microRNA microarray. 14 of thesesamples and 59 additional samples were profiled by qRT-PCR. Histologicalclassification of the study samples is summarized in Table 1a-b.

TABLE 1a Summary of sample types, numbers and histology used forcomparing liver tumors to non-liver tumors and liver metastases. NSample category Detail 30 Liver Primary Tumor 30 Liver (7 FFPE, 23fresh) 63 Non-Liver Primary Tumor 15 Breast (FFPE, 1 identified asadenocarcinoma) 14 Colon (FFPE, 10 identified as adenocarcinoma) 24 Lung(FFPE, 7 adenocarcinoma, 8 squamous cell carcinoma, 1 large cellcarcinoma, 3 NSCLC, 1 neuroendocrine SCLC, 1 mixed adeno-squamouscarcinoma) 5 Pancreas (FFPE, 4 exocrine adenocarcinoma and 1 pancreaticducr adenocarcinoma) 5 Stomach (FFPE, adenomcarcinoma) 46 LiverMetastasis of Known Origin 3 Breast (FFPE, adenocarcinoma) 36 Colon (35FFPE and 1 fresh, adenocarcinoma) 1 Lung (FFPE, adenocarcinoma) 2Pancreas (FFPE, adenocarcinoma) 3 Rectum (fresh, adenomcarcinoma) 1Stomach (FFPE, adenomcarcinoma)  5 Liver Metastasis of Unknown Origin 5Unknown (FFPE, adenocarcinoma) Additional samples in qRT-PCR Nvalidation set, by category Detail  5 Liver Primary Tumor 5 Liver (FFPE)18 Non-Liver Primary Tumor 2 Ovary and 16 Lung (FFPE)  1 LiverMetastasis of Known Origin 1 Kidney (FFPE)

TABLE 1b Summary of sample types, numbers and histology used forcomparing brain tumors to normal brain, non-brain tumors and brainmetastases Samples in microarray N data -by category Detail 15 Brainprimary tumors anaplastic astrocytoma (2), anaplastic oligodendroglioma(1), glioblastoma multiforme (7), low grade astrocytoma (3),oligodendroglioma (2) 187  Other primary tumors adipose liposarcoma (4),bladder (1 transitional cell carcinoma), breast (3 including 1infiltrating lobular carcinoma), cervix (3 adenocarcinoma, 2 squamouscell carcinoma), colon (4 adenocarcinoma), endometrium (7adenocarcinoma), esophagus (2 adenocarcinoma, 5 squamous cellcarcinoma), esophagus-stomach (7 adenocarcinoma), gallbladder (3adenocarcinoma), kidney (6 renal cell carcinoma), larynx (4 squamouscell carcinoma), liver (2 hepatocellular carcinoma), lung (7neuroendocrine carcinoid, 1 neuroendocrine large cell, 1 neuroendocrine;mix small cell-large cell, 7 neuroendocrine small cell, 8 non-small celladenocarcinoma, 3 non-small large cell carcinoma, 8 non-small squamouscell carcinoma, 7 pleura mesothelioma), lymphocytes (10 hodgkin'slymphoma), melanocytes (3 malignant melanoma), meninges (8 meningioma, 1atypical meningioma), mouth (4 squamous cell carcinoma, 1 keratinizingsquamous cell carcinoma), nose (4 squamous cell carcinoma, 1keratinizing squamous cell carcinoma), ovary (7 serous papillarycancer), pancreas (3 adenocarcinoma, 2 ductal adenocarcinoma, 2 exocrineadenocarcinoma), prostate (7 samples including 2 bph samples) smallintestine (7 stromal tumor, 1 adenocarcinoma), stomach adenocarcinoma(5), testis seminoma (3), thymus thymoma (3 type b2, 4 type b3), thyroid(4 carcinoma, 3 papillary carcinoma, 1 papillary tall cell carcinoma),tongue (2 squamous cell carcinoma, 8 keratinizing squamous cellcarcinoma), 50 Metastases in brain bladder (1 transitional cellcarcinoma), breast (2 carcinoma, 2 adenocarcinoma, 4 ductal carcinoma, 5infiltrating ductal carcinoma), colon (5 adenocarcinoma), endometrialtumor (1), kidney (2 clear cell carcinoma, 1 renal cell carcinoma), lung(10 including 1 carcinoma, 1 neuroendocrine small-cell carcinoma, 6non-small cell adenocarcinoma, 1 non-small squamous cell carcinoma),melanocytes (4 melanoma, 2 malignant melanoma), unknown (3 carcinoma, 5adenocarcinoma, 1 small cell carcinoma, 2 sarcoma), Additional samplesin N qRT-PCR validation set Detail 15 Brain primary tumors anaplasticoligodendroglioma (1), astrocytoma (5), glioblastoma qRT-PCR validationmultiforme (2), oligodendroglioma (7) set  8 Other primary tumorsbladder (1 transitional cell carcinoma), kidney (1 renal cellcarcinoma), liver (1 hepatocellular carcinoma), lung (2 including 1adenocarcinoma, 1 pleura mesothelioma), ovary (1 adenocarcinoma),pancreas (3 adenocarcinoma, 2 ductal adenocarcinoma, 2 exocrineadenocarcinoma), pancreas (1 neuroendocrine carcinoma), thymus thymoma(1 type b2) 10 Metastases in brain breast (2 ductal carcinoma), kidney(3 adenocarcinoma), lung (3 including 1 adenocarcinoma, 2 non-smallsquamous cell carcinoma), ovary (2 adenocarcinoma)

2. RNA Extraction

Total RNA was extracted from both the frozen and the FFPE tissues. Fromthe frozen tissues, a sample of approximately 0.5 cm³ was used per case.Total RNA was extracted using the miRvana miRNA isolation kit (Ambion)according to the manufacturer's instructions. Briefly, the sample washomogenized in a denaturing lysis solution followed by anacid-phenol:chloroform extraction and purification on a glass-fiberfilter.

From the FFPE samples, total RNA was isolated from seven to ten10-micron-thick tissue sections per case using the miRdictor™ extractionprotocol developed at Rosetta Genomics. Briefly, the sample wasincubated few times in xylene at 57° C. to remove paraffin excess,followed by ethanol washes. Proteins were degraded by proteinase Ksolution at 45° C. for few hours. The RNA was extracted with acidphenol:chloroform followed by ethanol precipitation and DNAse digestion.Total RNA quantity and quality was measured by Nanodrop ND-1000(NanoDrop Technologies, Wilmington, Del.).

3. miRdicator™ Array Platform

Custom microRNA microarrays were produced by printing DNAoligonucleotide probes representing ˜650 DNA oligonucleotide probesrepresenting microRNAs (Sanger database, version 9 and additionalRosetta validated and predicted miRNAs). Each probe, printed intriplicate, carries up to 22-nt linker at the 3′ end of the microRNA'scomplement sequence in addition to an amine group used to couple theprobes to coated glass slides. 20 μM of each probe were dissolved in2×SSC+0.0035% SDS and spotted in triplicate on Slide E coated microarrayslides (Schott Nexterion, Mainz, Germany) using the BioRoboticsMicroGrid II microarrater (Genomic Solutions, Ann Arbor, Mich.)according to the manufacturer's directions. 54 negative control probeswere designed using the sense sequences of different microRNAs. Twogroups of positive control probes were designed to hybridize tomiRdicator™ array (i) synthetic small RNA were spiked to the RNA beforelabeling to verify the labeling efficiency and (ii) probes for abundantsmall RNA (e.g. small nuclear RNAs (U43, U49, U24, Z30, U6, U48, U44),5.8s and 5s ribosomal RNA) are spotted on the array to verify RNAquality. The slides were blocked in a solution containing 50 mMethanolamine, 1M Tris (pH9.0) and 0.1% SDS for 20 min at 50° C., thenthoroughly rinsed with water and spun dry.

4. Cy-Dye Labeling of miRNA for Microarray

Up to 5 μg (mean: 4.5 μg) of total RNA were labeled by ligation of anRNA-linker, p-rCrU-Cy/dye (Dharmacon, Lafayette, Colo.), to the 3′-endwith Cy3 or Cy5. The labeling reaction contained total RNA, spikes(0.1-20 fmoles), 300 ng RNA-linker-dye, 15% DMSO, 1× ligase buffer and20 units of T4 RNA ligase (NEB New England Biolabs, Ipswich, Mass.) andproceeded at 4° C. for 1 hr followed by 1 hr at 37° C. The labeled RNAwas mixed with 3× hybridization buffer (Ambion, Austin, Tex.), heated to95° C. for 3 min and than added on top of the miR microarray. Slideswere hybridized 12-16 hr in 42° C., followed by two washes in roomtemperature with 1×SSC and 0.2% SDS and a final wash with 0.1×SSC.

Arrays were scanned using the Agilent DNA Microarray Scanner Bundle(Agilent Technologies, Santa Clara, Calif.) at resolution of 10 μm at100% power. Array images were analyzed using the SpotReader software(Niles Scientific, Portola Valley, Calif.).

5. Signal Calculation and Normalization of Microarray Data

The initial data set consisted of signals measured for multiple probesfor every sample. Triplicate spots were combined to one signal by takingthe logarithmic mean of the reliable spots. All data was log-transformed(natural base) and the analysis was performed in log-space. A referencedata vector for normalization R was calculated by taking the medianexpression level for each probe across all samples in each dataset. Foreach sample data vector S, a 2nd degree polynomial F was found so as toprovide the best fit between the sample data and the reference data,such that R≈F(S). Remote data points (“outliers”) were not used forfitting the polynomial F. For each probe in the sample (element S_(i) inthe vector S), the normalized value (in log-space) M_(i) is calculatedfrom the initial value S_(i) by transforming it with the polynomialfunction F, so that M_(i)=F(S_(i)). Data in FIGS. 1 and 2 was translatedback to linear-space by taking the exponent.

6. qRT-PCR

One microgram of total RNA was subjected to polyadenylation reaction asdescribed before (Shi and Chiang, 2005, Biotechniques, 39(4):519-25).Briefly, RNA was incubated in the presence of poly (A) polymerase (PAP)(Takara-2180A), MnCl₂, and ATP for 1 h at 37° C. Reverse transcriptionwas performed on the poly-adenylated product. An oligo-dT primerharboring a consensus sequence (complementary to the reverse primer) wasused for reverse transcription reaction. The primer is first annealed tothe poly A-RNA and then subjected to a reverse transcription reaction ofSuperScript II RT (Invitrogen). The cDNA was then amplified by real-timePCR reaction, using a miRNA-specific forward primer, TaqMan probe anduniversal reverse primer. The reactions were incubated for 10 min. at95° C. followed by 42 cycles of 95° C. for 15 s and 60° C. for 1 min.Normalizing the C_(t) values (per sample) by the C_(t) of either U6snRNA, the C_(t) of hsa-miR-24, or their average C_(t), shifted at mostone sample from each side in the test-set classification predictions.

7. Data Analysis and Statistics

Normalized expression values for each of the samples of the miRs werecalculated. P-values were calculated using two-sided t-test on thelogarithm of the signal.

The combined metric “L” was defined as the mean log 2 expression of twomicroRNAs, L≡[log 2(hsa-miR-141 signal)+log 2(hsa-miR-200c signal)]/2.This metric could be used to identify the non-liver primary tumorsamples with near-perfect accuracy. The receiver operatingcharacteristic curve (ROC curve) plots the sensitivity against one minusthe specificity, and is a measure of classification performance. Arandom classifier has an area under the curve (AUC) of 0.5, and anoptimal classifier with perfect sensitivity and specificity of 100% hasan area of 1. The ROC curve of the combination of hsa-miR-141 andhsa-miR-200c has an AUC of 0.999. The decision rule “classify asnon-liver when L>10” identified samples as non-liver primary tumors witha sensitivity of 98% and a specificity of 100%, with one pancreaticexocrine adenocarcinoma incorrectly classified as HCC. A more“conservative” cutoff at L=9.5 had a sensitivity of 98% and aspecificity of 93% (FIG. 1A), with two HCC samples incorrectlyidentified as non-HCC primary tumors. The same combined metric hadAUC=0.997 in identifying metastatic liver adenocarcinoma of a knownorigin from primary HCC (FIG. 1B), with sensitivity=98% and specificityof 93%. For the qRT-PCR data, L_(RT)≡[(hsa-miR-141 normalizedC_(t))+(hsa-miR-200c normalized C_(t))]/2 had AUC=1.

For comparing GI and non-GI primary tumors, the simple decision rule“classify as GI when the expression of hsa-miR-205 is smaller than halfthe expression of hsa-miR-194” (FIG. 2A) is accurate in all but one caseof a stomach primary tumor that is misclassified as non-GI by this rule.The metric (expression of hsa-miR-205)/(expression of hsa-miR-194) hadan AUC of 0.989.

For comparing brain tumors, the combined metric B₀ was defined as thesummed log 2 expression measured by microarray of hsa-miR-124 andhsa-miR-219-5p: B_(o)[log 2(hsa-miR-124 signal)+log 2(hsa-miR-219-5psignal)], and had AUC=1 when used to identify primary brain tumors fromother primary tumors, but had AUC=0.8987 when used to identify brainprimary tumors from brain metastases. The combined metric B₁ was definedas the summed log 2 expression measured by microarray of hsa-miR-9* andhsa-miR-92b: B₁≡[log 2(hsa-miR-9* signal)+log 2(hsa-miR-92b signal)],and had AUC=1 when used to identify primary brain tumors from otherprimary tumors or from brain metastases.

The combined metric B^(RT)* was defined as the summed log 2 expressionlevels measured by qRT-PCR data (the C_(t) values) of hsa-miR-9 andhsa-miR-92b: B^(RT)100-[C_(t)(hsa-miR-9)+C_(t)(hsa-miR-92b signal)], hadAUC=1 in the training set data and one error in the test-set data whenused to identify primary brain tumors from other primary tumors or frombrain metastases. The combined metric B^(RT*) was defined as the summedqRT-PCR C_(t) values of hsa-miR-9* and hsa-miR-92b:B^(RT*)100-[C_(t)(hsa-miR-9*)+C_(t)(hsa-miR-92b signal)], had AUC=1 inthe training set data and one error in the test-set data when used toidentify primary brain tumors from other primary tumors or from brainmetastases.

TABLE 2 miR and hairpin SEQ ID NOS: miR name MID HID hsa-miR-194 1 2, 3hsa-miR-205 4 5 hsa-miR-141 6 7 hsa-miR-200c 8 9 hsa-miR-200a 10 11hsa-miR-200b 12 13 hsa-miR-92b 14 15 hsa-miR-124a 16 17-19 hsa-miR-9* 2021-23 hsa-miR-219 24 25-26 hsa-miR-9 27 21-23 hsa-miR-128a 28 29hsa-miR-128b 30 31 hsa-miR- 122a 32 33 miR name: is the miRBase registryname (release 9.1). MID: is the SEQ ID NO of the mature microRNA. HID:is the SEQ ID NO of the microRNA hairpin precursor (Pre-microRNA).

TABLE 3 Primers and probes SEQ ID NOS: Fwd Primer MGB probe Rev PrimerTarget Sequence Sequence Sequence miR name SEQ ID NO: SEQ ID NO: SEQ IDNO: hsa-miR-124 34 39 44 hsa-miR-9 35 40 hsa-miR-9* 36 41 hsa-miR-92b 3742 U6 38 43

Example 1 Specific microRNAs are Able to Distinguish Between PrimaryNon-Hepatic and Hepatic Tumors

microRNA expression levels were profiled in 144 tumor samples including30 primary HCC samples, 63 primary tumors from epithelial origins, 46liver metastases from epithelial origins, and 5 adenocarcinomametastases to the liver from unknown origin. The primary HCC sampleswere compared to the other primary tumors and to the liver metastasessamples. Hsa-miR-122a (SEQ ID NO: 32), which is a highly liver-specificmicroRNA, had the strongest effect when comparing primary HCC tumors toother primary tumors with a fold-change>90, and could identify HCC fromother primary tumors (p-value=1.4e-38, AUC=1). However, this microRNA isalso found at high levels in the RNA extracted from liver metastases(FIG. 1A), ostensibly due to contamination from the adjacent normalliver tissue, and is not a good marker for identifying liver metastases(fold-change of medians 1.1, p-value=0.28, AUC=0.56). By usinghigh-throughput profiling, the microRNA family of hsa-miR-200a,b,c (SEQID NOS: 8, 10 and 12) and hsa-miR-141 (SEQ ID NO: 6) were identified asstrongly expressed in primary tumors from epithelial origins of commonliver metastases, but are not expressed in liver primary tumors(p-value<1e-11, AUC>0.9 for each). Because these microRNAs are notexpressed in the liver background, they are also useful indistinguishing between primary HCC tumors and metastatic tumors to theliver (p-value<1e-11, AUC>0.9 for each). For these microRNAs, unlikehsa-miR-122a, the expression level in the liver metastases is similar tothe expression level in the non-HCC primary tumors, in sharp contrast totheir expression level in the liver primary tumors (FIG. 1A).

Of this family of microRNAs, the strongest effect was found forhsa-miR-200c and hsa-miR-141 (FIG. 1B). Each of these microRNAs could beused to distinguish between primary HCC tumors and metastatic tumors tothe liver with very high accuracy (AUC>0.98). The expression level ofthese two microRNAs can be combined to create a powerful classifier. Thecombined metric L is defined as the sum of the logarithm (base 2) of thesignals of the two microRNAs, providing robustness to the classifier byadding two signals. A simple decision rule, “classify as HCC if L≦18,classify as non-HCC if L>18”, has a sensitivity of 98% in identifyingnon-HCC samples and a specificity of 93%, with only 4 errors of 144samples (AUC=0.9980). A more conservative classifier can be defined byallowing a margin for uncertainty, of factor 4 above or below thethreshold (equivalent to two cycles in qRT-PCR measurements). Theclassification rule “classify as HCC if L<16, classify as non-HCC ifL>20, leave unidentified if 16≦L≦20” leaves 7 samples of 144 (<5%)unclassified, and correctly classifies all other samples, including 5cases of metastatic liver adenocarcinoma of unknown origin (FIG. 1B).

These findings were validated by qRT-PCR, measuring the expressionlevels of these microRNAs in 31 samples including 24 new samples. TheqRT-PCR data showed an identical pattern, with hsa-miR-122a high in allsamples from the liver (FIG. 1C), and the miR-200 family specificallyhigh in samples of non-liver origin (FIG. 1C). Again, a simplecombination of hsa-miR-200c and hsa-miR-141 could identify primary frommetastatic liver tumors with near-perfect accuracy (FIG. 1D).

Similar results were obtained by using the combination of hsa-miR-200a(SEQ ID NO: 10) and hsa-miR-200b (SEQ ID NO: 12) (FIG. 3A-B,p-value<2*10̂-12 for each comparing hepatocellular carcinoma samples tonon-hepatic primary tumor samples or to metastatic liver adenocarcinomaof a known origin or to both together).

TABLE 4 microRNA expression in HCC primary tumors compared to theirexpression in other primary tumors and metastases, from microarray data.HCC primary liver tumors vs.: Compared to other Compared to metastasesCompared to both other primary tumors in liver primary and metastasesmicroRNA or metric: p- fold- p- fold- p- fold- value^(†) chng^(‡) AUCvalue^(†) chng^(‡) AUC value^(†) chng^(‡) AUC hsa-miR-122^(‡) 1.4E−3891.1 1.0000 2.8E−01 1.1 0.5623 6.3E−09 56.7 0.8193 hsa-miR-200a 4.8E−131/5.7 0.9185 7.3E−14 1/4.8 0.9492 1.3E−17 1/5.1 0.9277 hsa-miR-200b1.5E−15 1/5.7 0.9339 1.9E−12 1/4.3 0.9297 2.7E−19 1/4.6 0.9249hsa-miR-200c 1.7E−34 1/25 0.9947 8.9E−27 1/23 0.9877 1.0E−46 1/25 0.9921hsa-miR-141 1.7E−39 1/44 0.9979 1.2E−27 1/27 0.9905 1.5E−48 1/35 0.9950L^(†) 5.7E−38 1/989 0.9984 4.2E−29 1/627 0.9971 2.1E−50 1/866 0.9980^(†)P-values are calculated on log-signal of microRNA expression,measure by microarray, and on L which is in log-space (methods).^(‡)“fold-chng” is the fold change, calculated by the median signal inHCC divided by the median signal in other tissues. For hsa-miR-122a,fold change is greater than 1 indicating a higher expression in HCC. Forall other rows, signal is lower in HCC.

TABLE 5 microRNA expression in HCC primary tumors compared to theirexpression in other primary tumors and metastases, from qRT-PCR data.HCC primary liver tumors vs.: Compared to other Compared to metastasesCompared to both other primary tumors in liver primary and metastasesmicroRNA or metric: p- fold- p- fold- p- fold- value^(†) chng^(‡) AUCvalue^(†) chng^(‡) AUC value^(†) chng^(‡) AUC hsa-miR-122^(‡) 1.5E−105E+4 1.0000 8.3E−01 1.0 0.4857 2.6E−03 3E+4 0.8615 hsa-miR-200a 4.4E−091/883 1.0000 5.3E−03 1/89 0.9286 8.1E−07 1/542 0.9808 hsa-miR-200b2.5E−09 1/80 1.0000 1.5E−03 1/16 1.0000 4.0E−08 1/60 1.0000 hsa-miR-200c9.9E−14 1/643 1.0000 3.5E−05 1/678 1.0000 1.2E−12 1/661 1.0000hsa-miR-141 2.3E−11 1/323 1.0000 5.7E−04 1/164 1.0000 8.1E−10 1/3051.0000 L_(RT) ^(†) 3.0E−13 1/319 1.0000 1.1E−04 1/230 1.0000 1.2E−111/279 1.0000 ^(†)P-values are calculated on normalized C_(t) values ofmicroRNA expression by qRT-PCR, and L_(RT), which is the average C_(t)of hsa-miR-200c and hsa-miR-141 (see methods). ^(‡)“fold-chng” is thefold change, calculated by the median signal (2^(Ct)) in HCC divided bythe median signal (2^(Ct)) in other tissues. For hsa-miR-122a, foldchange is greater than 1 indicating a higher expression in HCC. For allother rows, signal is lower in HCC.

Example 2 Specific microRNAs are Able to Distinguish Between Primary GITumors and Non-GI Primary Tumors

The microRNA expression can provide further information on the possibleorigin of liver metastases. Another pair of microRNAs, hsa-miR-194 (SEQID NO: 1) and hsa-miR-205 (SEQ ID NO: 4), had significant differentexpression (p-value<1e-12 for each) in primary tumors fromgastrointestinal (GI) origin (14 colon, 5 pancreas, 5 stomach) comparedto primary tumors of non-GI epithelial origin (24 lung, 15 breast). Theratio of these expression levels could be used to accurately identifyprimary tumors from non-GI origin: the decision rule “classify as non-GIprimary when the expression of hsa-miR-205 is greater than half theexpression of hsa-miR-194” (FIG. 2A; dashed line marks the decisionboundary) had a sensitivity of 100% and specificity of 96% (AUC=0.9893).In the liver metastases, despite the small number of samples, this trendwas maintained (FIG. 2B). However, since hsa-miR-194 is also highlyexpressed in liver tissue, the contamination of these metastases by thesurrounding liver tissue raised significantly the expression ofhsa-miR-194, and thereby reduced the significance of its differentialexpression (p-value=0.0011 for hsa-miR-205, but only p-value=0.15 forhsa-miR-194). Nevertheless, a very high ratio of expression ofhsa-miR-194 to hsa-miR-205 is observed only in metastases of GI originand can thus be used for their identification (AUC=0.8988).

Example 3 Specific microRNAs are Able to Distinguish Between PrimaryBrain Tumors, Brain Metastases, Non-Brain Primary Tumors and NormalBrain Samples

microRNA expression levels were profiled on a microarray platform in 252tumor samples including 15 brain primary tumor samples, 187 non-brainprimary tumors, 50 brain metastases from various tissue origins and 2normal brain samples. The brain primary tumor samples were compared tothe other primary tumor samples, to normal brain samples and to samplesof brain-located metastases (Table 6). Hsa-miR-124 (SEQ ID NO: 16),which is highly specific to the nervous system, displayed the greatestdisparity in expression when comparing brain primary tumors to otherprimary tumors, with a fold-change of ˜100 (p-value=5.1e-57, AUC=0.9976,see Table 6). A combination of hsa-miR-124 and hsa-miR-219 (SEQ ID NO:24) (B₀, see methods) could be used to distinguish brain primary tumorsfrom non-brain primary tumors with 100% accuracy (FIG. 4A). Otherbrain-specific microRNAs such as hsa-miR-128a (SEQ ID NO: 28) andhsa-miR-128b (SEQ ID NO: 30) also showed very strong differentialexpression between brain primary tumors and other primary tumors(p-value<4e-28, AUC=0.9932, see FIG. 5A). However, these four microRNAsare highly expressed in normal brain and are also found at high levelsin RNA extracted from brain metastases (FIG. 4A). This latter effect,ostensibly due to contamination from the adjacent normal brain tissue,limits the utility of these microRNAs to serve as biomarkers fordifferentiating between brain primary tumors and brain-locatedmetastases (AUC of 0.85˜0.95, see Table 6).

In addition to the aforementioned four microRNAs (hsa-miR-124,hsa-miR-219-5p, hsa-miR-128a and hsa-miR-128b) hsa-miR-9* (SEQ ID NO:20) and hsa-miR-92b (SEQ ID NO: 14) are expressed specifically in braintumors and not expressed in other tumor types (FIG. 4B, AUC>0.99).Importantly, these two microRNAs also differentiate accurately betweenbrain primary tumors and metastatic tumors located in the brain(p-value<3e-18, AUC>0.99 for each) or normal brain samples. Indeed,using a combination of hsa-miR-9* and hsa-miR-92b expression (B₁, seemethods) it is possible to distinguish brain primary tumor samples fromall other samples with 100% accuracy in the microarray data (FIG. 4B). Asimple decision rule, “classify as primary brain tumor if B₁>19,classify as other if B₁<19”, identifies correctly all samples. A moreconservative classifier can be defined by allowing a margin foruncertainty of factor 2 above or below the threshold (equivalent to onecycle in qRT-PCR measurements). The classification rule “classify asbrain primary if B₁>20, classify as other if B₁<18, leave unidentifiedif 18≦B₁≦20” leaves only 2 samples out of 252 (<1%) as unclassified(FIG. 4B), and classifies correctly all other samples.

To validate these findings, 14 of these samples and 33 additionalsamples were profiled by qRT-PCR, for four potential biomarkers:hsa-miR-124, hsa-miR-9, hsa-miR-9* and hsa-miR-92b (Table 7), and twocontrols: hsa-miR-24, which was found to be relatively constantlyexpressed in the microarray data, and snRNA U6. These microRNAs showedthe same pattern as observed in the microarray data (Table 7).Hsa-miR-124 showed strong expression in the brain primary tumors, weakexpression in other primary tumors, and intermediate expression in themetastases (FIG. 6A). Thus, hsa-miR-124 was not a good candidate foridentifying metastatic tumors to the brain. On the other hand, hsa-miR-9(SEQ ID NO: 27), hsa-miR-9* (SEQ ID NO: 20), and hsa-miR-92b (SEQ ID NO:14) showed specific strong expression in primary brain tumors with lowerexpression in other tumors and in metastases to the brain (FIG. 6), withsignificant differences and strong separability between brain primarytumors and brain metastases (Table 7).

Combinations of hsa-miR-92b with either hsa-miR-9 (B^(RT)) or withhsa-miR-9* (B^(RT)*) were defined by summing their qRT-PCR C_(t) values(see Methods). A threshold was selected for classification for eachcombination using half of the samples as a training set. Theclassification accuracy was then tested on the second half of the dataset which was used as a test set. The classifications on the test setwere near perfect with one outlier of 23 samples, reaching 100% accuracyin identifying non-brain primary tumors from brain primary tumors, and88% sensitivity with 100% specificity in identifying metastatic braintumors from brain primary tumors, for both B^(RT) (FIG. 6B) and B^(RT)*.Indeed, these combinations show significant differences in expressionthat can be used to classify primary from metastatic brain tumors (Table7).

Based on these data, it is proposed that hsa-miR-9/9* and hsa-miR-92b,and their combination, represent new biomarkers that can be used toclassify brain malignancies—primary versus secondary.

The gene encoding hsa-miR-9/9* appears in the human genome in threeplaces, in chromosomes 1, 5, and 14, each an identical copy. Hsa-miR-92bis found on chromosome 1 and differs by only one nucleotide in its first20 from hsa-miR-92a, a member of the oncogenic miR-17-92 cluster.However, the expression patterns of hsa-miR-92a correlates only veryweakly with that of and hsa-miR-92b and does not enable classificationof brain malignancies (Table 6).

TABLE 6 Comparison between microRNA expression in primary brain tumorsand expression in other primary tumors or expression in brainmetastases, based on microRNA microarray data Primary brain vs. Otherprimary tumors Brain metastases microRNA or metric: fold- fold-p-value^(†) chng^(‡) AUC p-value^(†) chng^(‡) AUC hsa-miR-124 1.4E−5497.1 0.9975 5.4E−06 12.6 0.8600 hsa-miR-219-5p 9.7E−43 10.0 0.96794.1E−09 6.9 0.8840 B₀ ^(†) 1.8E−49 293.0 1.0000 9.0E−09 27.7 0.8987hsa-miR-128a 5.4E−27 9.3 0.9929 4.5E−11 4.2 0.9507 hsa-miR-128b 4.6E−319.0 0.9932 1.7E−11 4.5 0.9520 hsa-miR-9* 1.4E−64 31.3 1.0000 9.1E−2218.9 0.9933 hsa-miR-92b 1.8E−26 7.3 0.9993 2.1E−18 5.8 1.0000 B₁ ^(†)1.7E−57 205.9 1.0000 3.3E−26 128.7 1.0000 See Material and Methods fordefinitions. ^(†)P-values are calculated on log-signal of microRNAs, andon B₀ and B₁, which are in log-space. Less that 1000 probes were tested,and even after the more severe Bonferroni correction (multiplying eachp-value by ~1000), the p-values remain highly significant.^(‡)“fold-chng” is the fold change, calculated by dividing the mediansignal in brain primary tumors by the median signal in other tissues.

TABLE 7 Comparison between microRNA expression in primary brain tumorsand expression in other primary tumors or expression in brainmetastases, based on microRNA qRT-PCR data. Primary brain vs. Otherprimary tumors Brain metastases microRNA or metric: fold- fold-p-value^(†) chng^(‡) AUC p-value^(†) chng^(‡) AUC hsa-miR-124 4.7E−92144 1.0000 1.4E−4 48 0.8633 hsa-miR-9 2.3E−11 17648 1.0000 2.0E−11 5430.9833 hsa-miR-9* 1.5E−11 1887 1.0000 1.4E−12 415 0.9922 hsa-miR-92b1.7E−6 16 0.9542 7.2E−7 8 0.9219 B^(RT) 1.1E−10 2.9E+5 1.0000 7.7E−129993 0.9961 B^(RT)* 2.8E−10 11868 1.0000 6.4E−12 2428 1.0000^(†)P-values are calculated on measured Ct values and on B^(RT) andB^(RT)*, which are in log-space. Here only the listed 4 potentialbiomarkers and two combinations were tested, and no correction formultiple hypothesis testing is needed. ^(‡)“fold-chng” is the foldchange, calculated by converting the data to linear space (by taking theexponent base 2) and dividing the median signal in brain primary tumorsby the median signal in other tissues.

The foregoing description of the specific embodiments so fully revealsthe general nature of the invention that others can, by applying currentknowledge, readily modify and/or adapt for various applications suchspecific embodiments without undue experimentation and without departingfrom the generic concept, and, therefore, such adaptations andmodifications should and are intended to be comprehended within themeaning and range of equivalents of the disclosed embodiments. Althoughthe invention has been described in conjunction with specificembodiments thereof, it is evident that many alternatives, modificationsand variations will be apparent to those skilled in the art.Accordingly, it is intended to embrace all such alternatives,modifications and variations that fall within the spirit and broad scopeof the appended claims.

It should be understood that the detailed description and specificexamples, while indicating preferred embodiments of the invention, aregiven by way of illustration only, since various changes andmodifications within the spirit and scope of the invention will becomeapparent to those skilled in the art from this detailed description.

1. A method of classifying a cancer selected from the group consistingof liver cancer, brain cancer and gastrointestinal (GI) cancer, saidmethod comprising: (a) obtaining a biological sample from a subject; (b)determining an expression profile of a nucleic acid sequence selectedfrom the group consisting of SEQ ID NOS: 1-33, a fragment thereof and asequence having at least about 80% identity thereto from said sample;and (c) comparing said expression profile to a reference expressionprofile, wherein the results of said comparison allows forclassification of said specific cancer.
 2. The method of claim 1,wherein said liver cancer is hepatocellular carcinoma (HCC).
 3. Themethod of claim 1, wherein said gastrointestinal (GI) cancer is selectedfrom the group consisting of colon, pancreas and stomach cancer.
 4. Themethod of claim 1, wherein said brain cancer is selected from the groupconsisting of glioblastoma, astrocytoma and oligodendroglioma.
 5. Themethod of claim 1, wherein said biological sample is selected from thegroup consisting of bodily fluid, a cell line and a tissue sample. 6.The method of claim 5, wherein said tissue is a fresh, frozen, fixed,wax-embedded or formalin fixed paraffin-embedded (FFPE) tissue.
 7. Themethod of claim 1, wherein said method further comprises a classifieralgorithm.
 8. The method of claim 7, wherein said classifier is selectedfrom the group consisting of decision tree classifier, logisticregression classifier, nearest neighbor classifier, neural networkclassifier, Gaussian mixture model (GMM) and Support Vector Machine(SVM) classifier.
 9. The method of claim 1 for classifying liver cancer,the method comprising determining an expression profile of a nucleicacid sequence selected from the group consisting of SEQ ID NOS: 6-13,32-33, a fragment thereof and a sequence having at least about 80%identity thereto from said sample; and comparing said expression profileto a reference expression profile, wherein the results of saidcomparison allows for classifying said liver cancer.
 10. A method todistinguish between hepatocellular carcimoma (HCC) and metastasis to theliver, the method comprising obtaining a biological sample from asubject; determining an expression profile of a nucleic acid sequenceselected from the group consisting of SEQ ID NOS: 6-13, 32-33, afragment thereof and a sequence having at least about 80% identitythereto from said sample; and comparing said expression profile to areference expression profile, wherein said comparison is indicative ofhepatocellular carcimoma (HCC) or metastasis to the liver.
 11. Themethod of claim 10, wherein the metastasis to the liver isadenocarcinoma.
 12. The method of claim 1 for classifying brain cancer,the method comprising determining an expression profile of a nucleicacid sequence selected from the group consisting of SEQ ID NOS: -1427, afragment thereof and a sequence having at least about 80% identitythereto from said sample; and comparing said expression profile to areference expression profile, wherein the results of said comparisonallows for classifying said brain cancer.
 13. A method to distinguishbetween brain cancer and metastasis to the brain, the method comprisingobtaining a biological sample from a subject; determining an expressionprofile of a nucleic acid sequence selected from the group consisting ofSEQ ID NOS: 14-27, a fragment thereof and a sequence having at leastabout 80% identity thereto from said sample and comparing saidexpression profile to a reference expression profile, wherein saidcomparison is indicative of brain cancer and metastasis to brain. 14.The method of claim 1 for identifying gastrointestinal (GI) cancer, themethod comprising determining an expression profile of a nucleic acidsequence selected from the group consisting of SEQ ID NOS: 5-1, afragment thereof and a sequence having at least about 80% identitythereto from said sample; and comparing said expression profile to areference expression profile, wherein the results of said comparisonallows for classifying said GI cancer
 15. A method to distinguishbetween primary GI and non-GI tumor, the method comprising obtaining abiological sample from a subject; determining an expression profile of anucleic acid sequence selected from the group consisting of SEQ ID NOS:5-1, a fragment thereof and a sequence having at least about 80%identity thereto from said sample; and comparing said expression profileto a reference expression profile, wherein said comparison is indicativeof primary GI or non-GI tumor.
 16. The method of claim 15, wherein saidprimary GI tumor is selected from the group consisting of colon,pancreas and stomach tumor.
 17. The method of claim 15, wherein saidnon-GI tumor is selected from the group consisting of lung and breasttumor.
 18. A kit for cancer classification, wherein said cancer isselected from the group consisting of liver cancer, brain cancer andgastrointestinal (GI) cancer said kit comprising a probe comprising anucleic acid that is complementary to a sequence selected from the groupconsisting of SEQ ID NOS: 1-27, 32-33; a fragment thereof, and asequence having at least about 80% identity thereto.
 19. The kit ofclaim 18, wherein said cancer is brain cancer and said probe comprises asequence selected from the group consisting of SEQ ID NOS: 39-43, afragment thereof, and a sequence having at least about 80% identitythereto.
 20. The kit of claim 19, wherein the kit further comprises aforward primer selected from the group consisting of SEQ ID NOS: 34-38,a fragment thereof, and a sequence having at least about 80% identitythereto and a reverse primer comprising SEQ ID NO: 44.